Verwendete Pakete laden und installieren
install.packages("vegan")
install.packages("dpylr")
install.packages("ggplot2")
install.packages("nortest")
install.packages("ggpubr")
install.packages("cowplot")
install.packages("ggsignif")
install.packages("tidyverse")
install.packages("Hmisc")
install.packages("corrplot")
install.packages("PerformanceAnalytics")
install.packages("xts")
install.packages("quadprog")
install.packages("Rmisc")
library("dplyr")
library("vegan")
library("ggplot2")
library("nortest")
library("biomformat")
library("ggpubr")
library("cowplot")
library("ggsignif")
library("reshape2")
library("tidyverse")
library("Hmisc")
library("corrplot")
library("PerformanceAnalytics")
library("xts")
1.SCFA Analyse 1.1 Normalverteilung
SCFA_stool <- read.table("/Users/student05/Downloads/SCFA_stool total SCFA.txt", sep = '\t', comment='',head=T, row.names = 1)
View(SCFA_stool)
SCFA_stool<- add.rownames(SCFA_stool, "SampleID")
SCFA_stool$Time <-factor(SCFA_stool$Time, levels = c("PRE", "POST", "FOLLOWUP"))
SCFA_stool[1,3]<- "PRE"
SCFA_stool[1,4]<- "OU1"
scfa_colnames <- colnames(SCFA_stool[, c(6:10)])
nd.SCFA<- data_frame()
scfa
scfa_colnames[1]
for (i in scfa_colnames) {
fit <- shapiro.test(as.matrix(as.data.frame(lapply(SCFA_stool[,i],
as.numeric))))
p = fit$p.value
nrow = nrow(nd.SCFA)+1
nd.SCFA[nrow, "column"] = i
nd.SCFA[nrow, "p.value"] = round(p, 4)
}
sign.nd_SCFA <- filter(nd.SCFA, p > 0.05)
ggqqplot(SCFA_stool$Acetate, ylab = "Acetate concentration nmol/mg", xlab = "SampleID")
ggqqplot(SCFA_stool$Iso.Butyrate, ylab = "Iso-Butyrate concentration nmol/mg", xlab = "SampleID")
ggqqplot(SCFA_stool$Propionate, ylab = "Propionate concentration nmol/mg", xlab = "SampleID")
ggqqplot(SCFA_stool$Butyrate, ylab = "Butyrate concentration nmol/mg", xlab = "SampleID")
Filtern der SCFA-Daten nach PRE und POST Proben
SCFA_stool_pairs <- filter(SCFA_stool, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
SCFA_stool_pairs_PP <- filter(SCFA_stool_pairs, Time=="PRE" | Time=="POST")
SCFA_stool_pairs_PPFU <- filter(SCFA_stool, Proband == "05AP" | Proband == "13
BS" | Proband == "17SK" | Proband == "22WS" | Proband ==
"40WA" | Proband == "41ML" | Proband == "54SL")
Wilcoxon-Test zwischen den Zeitpunkten der SCFA
PRE und POST
wilcox_SCFA<- data_frame()
for (i in scfa_colnames) {
tmp<- filter(SCFA_stool_pairs_PP[ ,i])
x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y <- SCFA_stool_pairs_PP$Time
tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = TRUE)
p <- tmp_wilcox$p.value
nrow = nrow(wilcox_SCFA)+1
wilcox_SCFA[nrow, "SCFA"] <- i
wilcox_SCFA[nrow, "Mean PRE"] <- round(apply(subset(filter(SCFA_stool_pairs, Time == "PRE")[,i], na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
wilcox_SCFA[nrow, "sd PRE"] <- round(apply(subset(filter(SCFA_stool_pairs,Time == "PRE")[,i], na.rm = TRUE), 2, sd,na.rm = TRUE), 4)
wilcox_SCFA[nrow, "Mean POST"] <-round(apply(subset(filter(SCFA_stool_pairs,Time == "POST")[,i], na.rm = TRUE), 2, mean,na.rm = TRUE), 4)
wilcox_SCFA[nrow, "sd POST"] <- round(apply(subset(filter(SCFA_stool_pairs,Time == "POST")[,i], na.rm = TRUE), 2, sd,na.rm = TRUE), 4)
wilcox_SCFA[nrow, "p.value"] <- round(p, 4) }
Acetate p.value = 0.025 -> signifikanter Unterschied!, mean PRE = 205.3, sd PRE = 148.3, mean POST = 132.58, sd POST = 79 Propionate p=0.136 -> kein signifikanter Unterschied!, mean PRE = 78.4, sd PRE = 62.7, mean POST = 54.3, sd POST = 33.1 Butyrate p-value = 0.346 -> kein signifikanter Unterschied!, mean PRE = 59.2, sd PRE = 41.4, mean POST = 44.5, sd POST = 27 Isobutyrate p-value = 0.571 -> kein signifikanter Unterschied!, mean PRE = 9.39, sd PRE = 4.55, mean POST = 9.17, sd POST = 3.1
Wilcoxon-Test Follow-up
wilcox_SCFA_FU <- data_frame()
for (i in scfa_colnames) {
tmp <- filter(SCFA_stool_pairs_PPFU[,i], !is.na(i))
x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y <- SCFA_stool_pairs_PPFU$Time
tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = TRUE)
p_POST_FU <- tmp_wilcox$p.value[1]
p_PRE_FU <- tmp_wilcox$p.value[2]
p_PRE_POST <- tmp_wilcox$p.value[4]
nrow = nrow(wilcox_SCFA_FU)+1
wilcox_SCFA_FU[nrow, "SCFA"] <- i
wilcox_SCFA_FU[nrow, "Mean FOLLOWUP"] <- round(apply(subset(filter(SCFA_stool_pairs_PPFU, Time == "FOLLOWUP")[,i], na.rm = TRUE),2, mean, na.rm = TRUE), 4)
wilcox_SCFA_FU[nrow, "sd FOLLOWUP"] <- round(apply(subset(filter(SCFA_stool_pairs_PPFU, Time =="FOLLOWUP")[,i], na.rm = TRUE), 2, sd, na.rm = TRUE), 4)
wilcox_SCFA_FU[nrow, "p.value_POST_FU"] <- round(p_POST_FU, 2)
wilcox_SCFA_FU[nrow, "p.value_PRE_FU"] <- round(p_PRE_FU, 2)
wilcox_SCFA_FU[nrow, "p.value_PRE_POST"] <- round(p_PRE_POST, 2)
}
Acetate p.valuePOST/FU = 0.56, p.valuePRE/FU = 0.47, p.valuePRE/POST = 0.47 -> alles kein signifikanter Unterschied! mean FU = 173, sd FU = 43.7 Propionate p.valuePOST/FU = 0.94, p.valuePRE/FU = 0.94, p.valuePRE/POST = 0.94 -> alles kein signifnikanter Unterschied! mean FU = 58.7, sd FU= 10.1 Butyrate p.valuePOST/FU = 0.7, p.valuePRE/FU = 0.7, p.valuePRE/POST = 0.7 -> kein signifikanter Unterschied bei allen! mean FU = 43.5, sd FU = 17.9 Isobutyrate p.valuePOST/FU = 1, p.valuePRE/POST = 1, p.valuePRE/FU = 1 -> bei allen kein signifikanter Unterschied! mean FU = 9.14, sd FU = 2.13
Plotten aller SCFA Alle Zeiten zusammen
SCFA_stool.melt <- melt(SCFA_stool_pairs, id.vars = 'Time', measure.vars = c('Acetate', 'Propionate', 'Butyrate', 'Iso.Butyrate'))
SCFA_stool.melt <- subset(filter(SCFA_stool.melt, !Time == 'FOLLOWUP'))
SCFA_stool.melt <- dplyr::rename(SCFA_stool.melt, SCFA=variable)
SCFA_stool.melt <- dplyr::rename(SCFA_stool.melt, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/SCFA.alltimes.neu.pdf",width=6, height=10)
ggplot(SCFA_stool.melt,aes(x=SCFA, y=Concentration, fill= SCFA)) +
xlab ('SCFA') + ylab ('Konzentrationen [µmol/g]') +
geom_boxplot(width = .4, lwd=1) + theme_classic()+
scale_fill_manual(labels = c("Acetate", "Propionate", "Butyrate", "Iso-Butyrate"), values = c("seagreen4", "seagreen3", "seagreen2", "seagreen1"))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
dev.off()
Plotten der SCFA je Zeitpunkt
pdf("/Users/student05/Documents/fertige Plots/SCFA.times.pdf",width=6, height=10)
ggplot(SCFA_stool.melt,aes(x=Time, y=Concentration, fill= SCFA)) +
xlab ('Zeitpunkt') + ylab ('Konzentrationen [µmol/g]') +
geom_boxplot(width = 0.8, lwd=1) +
scale_fill_manual(labels = c("Acetate", "Propionate", "Butyrate", "Iso-Butyrate"), values = c("seagreen4", "seagreen3", "seagreen2", "seagreen1"))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text( hjust=1))+
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
dev.off()
Plotten der einzelnen SCFA Acetat
Acetate_stool.melt <- melt(SCFA_stool_pairs, id.vars = 'Time', measure.vars = c('Acetate'))
Acetate_stool.melt <- rename(Acetate_stool.melt, SCFA=variable)
Acetate_stool.melt <- rename(Acetate_stool.melt, Concentration=value)
ggplot(Acetate_stool.melt) +
xlab ('Time Point') + ylab ('Concentration [mg/ml]') +
geom_boxplot(aes(x=Time, y=Concentration, fill=SCFA)) +
scale_fill_manual(labels = c("Acetate"), values = c("tomato")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons = list(c("PRE","POST")))
Boxplot mit je 2 SCFA je Zeitpunkt, linked by Probands
Acetat-Propionat
SCFA_stool_melt_AP <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Acetate', 'Propionate'))
SCFA_stool_melt_AP <- rename(SCFA_stool_melt_AP, Concentration=value)
SCFA_stool_melt_AP <- rename(SCFA_stool_melt_AP, SCFA=variable)
ggpaired(SCFA_stool_melt_AP, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') +
xlab('SCFA') + ylab('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Acetate", "Propionat"), values = c("yellowgreen", "steelblue2"))
Acetat-Butyrat
SCFA_stool_melt_AB <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Acetate', 'Butyrate'))
SCFA_stool_melt_AB <- rename(SCFA_stool_melt_AB, Concentration=value)
SCFA_stool_melt_AB <- rename(SCFA_stool_melt_AB, SCFA=variable)
ggpaired(SCFA_stool_melt_AB, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') +
xlab('SCFA') + ylab('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Acetate", "Butyrate"), values = c("yellowgreen", "coral2"))
Acetat-Isobutyrat
SCFA_stool_melt_AI <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Acetate', 'Iso.Butyrate'))
SCFA_stool_melt_AI <- rename(SCFA_stool_melt_AI, Concentration=value)
SCFA_stool_melt_AI <- rename(SCFA_stool_melt_AI, SCFA=variable)
ggpaired(SCFA_stool_melt_AI, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') +
xlab('SCFA') + ylab('Concentration [mg/ml]')+
scale_fill_manual(labels=c("Acetate", "Iso.Butyrate"), values = c("yellowgreen", "deeppink"))
Propionat-Butyrat
SCFA_stool_melt_PB <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Propionate', 'Butyrate'))
SCFA_stool_melt_PB <- rename(SCFA_stool_melt_PB, Concentration=value)
SCFA_stool_melt_PB <- rename(SCFA_stool_melt_PB, SCFA=variable)
ggpaired(SCFA_stool_melt_PB, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') +
xlab('SCFA') + ylab('Concentration [mg/ml]')+
scale_fill_manual(labels=c("Propionate", "Butyrate"), values = c("steelblue2", "coral2"))
Butyrat-Isobutyrat
SCFA_stool_melt_BI <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Butyrate', 'Iso.Butyrate'))
SCFA_stool_melt_BI <- rename(SCFA_stool_melt_BI, Concentration=value)
SCFA_stool_melt_BI <- rename(SCFA_stool_melt_BI, SCFA=variable)
ggpaired(SCFA_stool_melt_BI, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') +
xlab('SCFA') + ylab('Concentration [mg/ml]')+
scale_fill_manual(labels=c("Butyrate", "Iso.Butyrate"), values = c("coral2", "deeppink"))
Propionat-Isobutyrat
SCFA_stool_melt_PI <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Propionate','Iso.Butyrate'))
SCFA_stool_melt_PI <- rename(SCFA_stool_melt_PI, Concentration=value)
SCFA_stool_melt_PI <- rename(SCFA_stool_melt_PI, SCFA=variable)
ggpaired(SCFA_stool_melt_PI, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') +
xlab('SCFA') + ylab('Concentration [mg/ml]')+
scale_fill_manual(labels=c("Propionate", "Iso.Butyrate"), values = c("steelblue2", "deeppink"))
1.2 Erstellen einer Korrelationsmatrix zum Testen von Korrelationen zwischen den SCFA
SCFA_stool <- read.table("/Users/student05/Downloads/SCFA_stool total SCFA.txt", sep = '\t', comment='',
head=T, row.names = 1)
write.table(SCFA_stool, file ='/Users/student05/Documents/SCFA/SCFA_stool total SCFA.txt',sep ="\t", col.names = TRUE, row.names = FALSE)
View(SCFA_stool)
SCFA_stool<- add_rownames(SCFA_stool, "SampleID")
SCFA_stool$Time <-factor(SCFA_stool$Time, levels = c("PRE", "POST", "FOLLOWUP"))
SCFA_stool_matrix_PRE <- subset(filter(SCFA_stool, Time == "PRE"))[ ,6:10]
SCFA_stool_matrix_POST <- subset(filter(SCFA_stool, Time == "POST"))[ ,6:10]
res.PRE <- cor(SCFA_stool_matrix_PRE)
res.POST <- cor(SCFA_stool_matrix_POST)
Spearman-Korrelation
res2.PRE <- rcorr(as.matrix(SCFA_stool_matrix_PRE), type = "spearman")
res2.POST <- rcorr(as.matrix(SCFA_stool_matrix_POST), type = "spearman")
Korrelationskoeffizient bestimmen
res2.PRE$r
res2.POST$r
SCFA_stool_PRE_CC <- as.matrix((res2.PRE$r))
SCFA_stool_POST_CC <- as.matrix(res2.POST$r)
p-values bestimmen
res2$P
SCFA_stool_PRE_PV <- as.matrix(res2.PRE$P)
SCFA_stool_POST_PV <- as.matrix(res2.POST$P)
flattenCorrMatrix erstellen für PRE und POST
flattenCorrMatrix.PRE <- function(SCFA_stool_PRE_CC, SCFA_stool_PRE_PV) {
ut <- upper.tri(SCFA_stool_PRE_CC)
data.frame(
row = rownames(SCFA_stool_PRE_CC)[row(SCFA_stool_PRE_CC)[ut]],
column = rownames(SCFA_stool_PRE_CC)[col(SCFA_stool_PRE_CC)[ut]],
cor =(SCFA_stool_PRE_CC)[ut],
p = SCFA_stool_PRE_PV[ut]
)
}
flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P)
flattenCorrMatrix.POST <- function(SCFA_stool_POST_CC, SCFA_stool_POST_PV) {
ut <- upper.tri(SCFA_stool_POST_CC)
data.frame(
row = rownames(SCFA_stool_POST_CC)[row(SCFA_stool_POST_CC)[ut]],
column = rownames(SCFA_stool_POST_CC)[col(SCFA_stool_POST_CC)[ut]],
cor =(SCFA_stool_POST_CC)[ut],
p = SCFA_stool_POST_PV[ut]
)
}
flattenCorrMatrix.POST(res2.POST$r, res2.POST$P)
Dataframe erstellen
SCFA_PRE_cor.p <- as.data.frame(flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P))
SCFA_POST_cor.p <- as.data.frame(flattenCorrMatrix.POST(res2.POST$r, res2.POST$P))
colnames(SCFA_PRE_cor.p) <- c("SCFA", "SCFA", "correlation coefficient", "p-value")
colnames(SCFA_POST_cor.p) <- c("SCFA", "SCFA", "correlation coefficient", "p-value")
Correlogram erstellen (Package corrplot)
corrplot(res.PRE, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
corrplot(res.POST, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
corrplot(res2.PRE$r, type="upper", order="hclust",
p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
corrplot(res2.PRE$r, type="upper", order="hclust",
p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
Scatter plots erstellen
chart.Correlation(SCFA_stool_matrix_PRE, histogram=TRUE, pch=19)
chart.Correlation(SCFA_stool_matrix_POST, histogram = T, pch = 19)
heatmap erstellen
col<- colorRampPalette(c("blue", "white", "red"))(20)
heatmap(x = res.PRE, col = col, symm = TRUE)
1.3 Korrelationen zwischen SCFA und Ballaststoffaufnahme
SCFA_stool.f <- read.table("/Users/student05/Documents/SCFA/scfa.fibre.txt", sep = '\t', comment='',head=T, row.names = 1)
SCFA_stool.f <- subset(filter(SCFA_stool.f, !Proband == '33MP'))
SCFA_stool.f[1,3]<- "PRE"
SCFA_stool.f$Time <-factor(SCFA_stool.f$Time, levels = c("PRE", "POST"))
Korrelationen zwischen allen SCFA und Ballaststoffaufnahme In Arbeit
pdf("/Users/student05/Documents/fertige Plots/SCFA.Ballaststoffe.pdf",width=8, height=10)
ggscatter(SCFA_stool.f, x='Total.SCFA', y='Fibre', palette = c('tomato', 'yellowgreen'), add = 'reg.line', color = "grey59",fill = "lightgray",conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, 80),cor.coef.size = 8, xlab= 'Gesamt-SCFA Konzentrationen [µmol/g]', ylab = 'Ballaststoffaufnahme [g]')+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
geom_point(color='black', size=2.5)+
theme(legend.position="none")
dev.off()
cortest einzelne SCFA
cor.test(subset(filter(SCFA_stool.f))$Total.SCFA, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
cor.test(subset(filter(SCFA_stool.f))$Acetate, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
cor.test(subset(filter(SCFA_stool.f))$Propionate, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
cor.test(subset(filter(SCFA_stool.f))$Butyrate, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
cor.test(subset(filter(SCFA_stool.f))$Iso.Butyrate, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
p.adjust(c(0.1407,0.1844,0.2612, 0.06335, 0.986 ), method = 'BH', n=5)
Plotten Acetat-Balasststoffaufnahme
ggscatter(SCFA_stool.f, x='Acetate', y='Fibre',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Acetate concentration [nmol/g]', ylab = 'Fiber intake [g]')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
1.4 High und Low Sterolkonvertierungstypen SCFA
Nach Konvertierungstypen filtern und PRE und POST Proben zusammenfuegen
lowconv <- filter(SCFA_stool, Proband == "05AP" | Proband == "33MP"
| Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
| Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")
lowconv['Phenotype'] = 'low converter'
highconv <- filter(SCFA_stool, Proband == "06WT" | Proband == "07RW"
| Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
| Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
| Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
| Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
highconv['Phenotype'] = 'high converter'
highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL
noconv <- filter(SCFA_stool, Proband == "28HM" | Proband == "32FG"
| Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
| Proband == "39DA" | Proband == "66DG" | Proband == "70PL")
noconv['Phenotype'] = 'not classified'
noconv$Converter.Type <- NULL
convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)
convT_paired <- filter(convT, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
convT_paired_PP <- filter(convT_paired, Time=="PRE" | Time=="POST")
Boxplot SCFA je Sterolkonvertierungstyp
alle SCFA
SCFA_stool.melt.CT <- melt(convT, id.vars = 'Phenotype', measure.vars = c('Acetate', 'Propionate', 'Butyrate', 'Iso.Butyrate'))
SCFA_stool.melt.CT <- rename(SCFA_stool.melt.CT, SCFA=variable)
SCFA_stool.melt.CT <- rename(SCFA_stool.melt.CT, Concentration=value)
ggplot(SCFA_stool.melt.CT,aes(x=Phenotype, y=Concentration, fill= SCFA)) +
xlab ('Converter type') + ylab ('Concentration [mg/ml]') +
geom_boxplot() +
scale_fill_manual(labels = c("Acetate", "Propionate", "Butyrate", "Iso-Butyrate"), values = c("tomato", "yellowgreen", "steelblue2", "orchid2"))
ggplot(subset(filter(convT, !Phenotype == "not classified")), aes(x=Phenotype, y=Acetate)) +
xlab ('Phenotype') + ylab('Acetate Concentration [mg/ml]')+
geom_boxplot(fill = 'whitesmoke', color = 'black')+
geom_dotplot(bonaxis = 'y', stackdir = 'center', dotsize = 0.3, fill= 'grey22')
Acetat
Acetate_stool.melt.CT <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('Acetate'))
Acetate_stool.melt.CT <- rename(Acetate_stool.melt.CT, SCFA=variable)
Acetate_stool.melt.CT <- rename(Acetate_stool.melt.CT, Concentration=value)
ggplot(filter(Acetate_stool.melt.CT, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Acetate"), values = c("yellowgreen"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
Propionat
Propionate_stool.melt.CT <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('Propionate'))
Propionate_stool.melt.CT <- rename(Propionate_stool.melt.CT, SCFA=variable)
Propionate_stool.melt.CT <- rename(Propionate_stool.melt.CT, Concentration=value)
ggplot(filter(Propionate_stool.melt.CT, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Propionat"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
Butyrat
Butyrate_stool.melt.CT <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('Butyrate'))
Butyrate_stool.melt.CT <- rename(Butyrate_stool.melt.CT, SCFA=variable)
Butyrate_stool.melt.CT <- rename(Butyrate_stool.melt.CT, Concentration=value)
ggplot(filter(Butyrate_stool.melt.CT, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Butyrate"), values = c("coral2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
Isobutyrat
Iso.Butyrate_stool.melt.CT <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('Iso.Butyrate'))
Iso.Butyrate_stool.melt.CT <- rename(Iso.Butyrate_stool.melt.CT, SCFA=variable)
Iso.Butyrate_stool.melt.CT <- rename(Iso.Butyrate_stool.melt.CT, Concentration=value)
ggplot(filter(Iso.Butyrate_stool.melt.CT, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Iso.Butyrate"), values = c("deeppink"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
Plot p-value zwischen Sterolconverter bei einem Zeitpunkt
ggplot(filter(Acetate_stool.melt.CT, !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Acetate"), values = c("yellowgreen"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
ggplot(filter(Propionate_stool.melt.CT, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= SCFA)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Propionate"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))
ggplot(filter(Butyrate_stool.melt.CT, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= SCFA)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Butyrate"), values = c("coral2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))
ggplot(filter(Iso.Butyrate_stool.melt.CT, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= SCFA)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Iso.Butyrate"), values = c("deeppink"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))
1.5 Diversitaetsanalysen SCFA- Shannon/Simpson Daten Laden
SCFA_stool <- read.table("/Users/student05/Downloads/SCFA_stool total SCFA.txt", sep = '\t', comment='',
head=T, row.names = 1)
map_alphadiv <- read.table("/Users/student05/Documents/txt dateien r/means_alpha_div.txt", sep = '\t', comment='',head = TRUE, row.names = 1)
Filtern fuer PRE und POST Proben
SCFA_stool$Time <-factor(SCFA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))
SCFA_stool[1,4]<- "OU1"
SCFA_stool_pairs <- filter(SCFA_stool, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "32FG" | Proband == "36ER" | Proband == "35AD"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "57MT" | Proband == "69HL")
write.table(SCFA_stool_pairs, file = '/Users/student05/Documents/SCFA/SCFA analyse/OTU SCFA analyse/SCFA_stool.pairs Shannon Simpson.txt', sep ="\t", col.names= TRUE,row.names = FALSE)
SCFA_stool_pairs_PP <- filter(SCFA_stool_pairs, Time=="PRE" | Time=="POST")
write.table(SCFA_stool_pairs_PP, file = '/Users/student05/Documents/SCFA/SCFA analyse/OTU SCFA analyse/SCFA_stool.pairs.PP Shannon Simpson.txt', sep ="\t", col.names= TRUE,row.names = FALSE)
Shannon und Simpson einfuegen in SCFA Datensatz
common.ids.St <- intersect(rownames(SCFA_stool), rownames(map_alphadiv))
common.ids.St <- intersect(row.names(SCFA_stool), row.names(map_alphadiv))
SCFA_stool <- SCFA_stool[common.ids.St,]
map_alphadiv <- map_alphadiv[common.ids.St,]
SCFA_stool$Shannon <- map_alphadiv$Shannon
SCFA_stool$Simpson <- map_alphadiv$Simpson
Korrelationsanalysen zwischen SCFA und Shannon Erstellen von Matrix und Loop, filtern fuer Signifikanz
corr_colnames_SCFA <-colnames(SCFA_stool[,6:9])
corr_spearman_Shannon_SCFA <- data.frame()
for( i in unique(corr_colnames_SCFA)) {
tmp <- filter(SCFA_stool, !is.na(i))
x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y = t(as.matrix(tmp$Shannon) )
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
w = t(as.matrix(subset(filter(tmp, Time == "PRE"))$Shannon))
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Shannon))
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
a = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "FOLLOW-UP"))[,i],as.numeric)))
b = t(as.matrix(subset(filter(tmp, Time == "FOLLOW-UP"))$Shannon))
tmp_corr_spearman_FU <- cor.test(a, b, method="spearman")
rho_FU = tmp_corr_spearman_FU$estimate
p_FU = tmp_corr_spearman_FU$p.value
nrow = nrow(corr_spearman_Shannon_SCFA)+1
corr_spearman_Shannon_SCFA[nrow,"Div"] = "Shannon"
corr_spearman_Shannon_SCFA[nrow, "column"] = i
corr_spearman_Shannon_SCFA[nrow, "rho"] = rho
corr_spearman_Shannon_SCFA[nrow, "p.value"] = p
corr_spearman_Shannon_SCFA[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Shannon_SCFA[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Shannon_SCFA[nrow, "rho_POST"] = rho_POST
corr_spearman_Shannon_SCFA[nrow, "p.value_POST"] = p_POST
corr_spearman_Shannon_SCFA[nrow, "rho_FU"] = rho_FU
corr_spearman_Shannon_SCFA[nrow, "p.value_FU"] = p_FU
}
corr_spearman_Shannon_SCFA$p.adjusted <- p.adjust(corr_spearman_Shannon_SCFA$p.value, method = "BH", n = 5)
corr_spearman_Shannon_SCFA$p.adjusted_PRE <-p.adjust(corr_spearman_Shannon_SCFA$p.value_PRE, method = "BH", n = 5)
corr_spearman_Shannon_SCFA$p.adjusted_POST <- p.adjust(corr_spearman_Shannon_SCFA$p.value_POST, method = "BH", n = 5)
corr_spearman_Shannon_SCFA$p.adjusted_FU <- p.adjust(corr_spearman_Shannon_SCFA$p.value_FU, method = "BH", n = 5)
corr_sig_Shannon_SCFA <- filter(corr_spearman_Shannon_SCFA, p.adjusted < 0.05 | p.adjusted_PRE < 0.5 | p.adjusted_POST < 0.5 | p.adjusted_FU < 0.5)
write.table(corr_sig_Shannon_SCFA, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/SCFA.Shannon.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
-> no SCFA has signficicant p-value Acetate has the best p-value with 0.06 < 0.05
Plot Korrelation Acetat/Total scfa und Shannon
ggplot(SCFA_stool, aes(x=Acetate, y=Shannon)) + geom_point(aes(color=Time)) +
scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('Shannon-Index')
ggplot(SCFA_stool, aes(x=Total.SCFA, y=Shannon)) + geom_point(aes(color=Time)) +
scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total SCFA Concentration [mg/ml]') +
ylab('Shannon-Index')
SCFA_stool <- subset(filter(SCFA_stool, !Time== 'FOLLOW-UP'))
ggscatter(SCFA_stool_pairs_PP, x='Acetate', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Acetate Concentration [mg/ml]', ylab = 'Shannon-Index') +
facet_wrap(~Time, scales = "free_x")+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(SCFA_stool, x='Total.SCFA', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(0, 7),xlab= 'Total SCFA Concentration [µmol/g DW]', ylab = 'Shannon-Index')+
facet_wrap(~Time, scales = "free_x")+
theme(legend.position="none")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))
Korrelationsanalysen zwischen SCFA und Simpson Erstellen von Matrix und Loop, filtern fuer Signifikanz
corr_spearman_Simpson_SCFA <- data.frame()
for( i in unique(corr_colnames_SCFA)) {
tmp <- filter(SCFA_stool, !is.na(i))
x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y = t(as.matrix(tmp$Simpson))
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
w = t(as.matrix (subset(filter(tmp, Time == "PRE"))$Simpson))
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Simpson))
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
a = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "FOLLOW-UP"))[,i],as.numeric)))
b = t(as.matrix(subset(filter(tmp, Time == "FOLLOW-UP"))$Simpson))
tmp_corr_spearman_FU <- cor.test(a, b, method="spearman")
rho_FU = tmp_corr_spearman_FU$estimate
p_FU = tmp_corr_spearman_FU$p.value
nrow = nrow(corr_spearman_Simpson_SCFA)+1
corr_spearman_Simpson_SCFA[nrow,"Div"] = "Simpson"
corr_spearman_Simpson_SCFA[nrow, "column"] = i
corr_spearman_Simpson_SCFA[nrow, "rho"] = rho
corr_spearman_Simpson_SCFA[nrow, "p.value"] = p
corr_spearman_Simpson_SCFA[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Simpson_SCFA[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Simpson_SCFA[nrow, "rho_POST"] = rho_POST
corr_spearman_Simpson_SCFA[nrow, "p.value_POST"] = p_POST
corr_spearman_Simpson_SCFA[nrow, "rho_FU"] = rho_FU
corr_spearman_Simpson_SCFA[nrow, "p.value_FU"] = p_FU
}
corr_spearman_Simpson_SCFA$p.adjusted <- p.adjust(corr_spearman_Simpson_SCFA$p.value,method = "BH", n = 5)
corr_spearman_Simpson_SCFA$p.adjusted_PRE <-p.adjust(corr_spearman_Simpson_SCFA$p.value_PRE, method = "BH", n = 5)
corr_spearman_Simpson_SCFA$p.adjusted_POST <- p.adjust(corr_spearman_Simpson_SCFA$p.value_POST, method = "BH", n = 5)
corr_spearman_Simpson_SCFA$p.adjusted_FU <- p.adjust(corr_spearman_Simpson_SCFA$p.value_FU, method = "BH", n = 5)
corr_sig_Simpson_SCFA <- filter(corr_spearman_Simpson_SCFA, p.adjusted < 0.05 | p.adjusted_PRE < 0.5 | p.adjusted_POST < 0.5 | p.adjusted_FU < 0.5)
write.table(corr_sig_Simpson_SCFA, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/SCFA.Simpson.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
no metadata has significant p-value Propionat has lowest p-value 0.10 > 0.05
Plot metadata fuer Propionate/Total scfa und Simpson
ggplot(SCFA_stool, aes(x=Propionate, y=Simpson)) + geom_point(aes(color=Time))+
scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('Simpson-Index')
ggplot(SCFA_stool, aes(x=Total.SCFA, y=Simpson)) + geom_point(aes(color=Time))+
scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total SCFA Concentration [mg/ml]') +
ylab('Simpson-Index')
ggscatter(SCFA_stool, x='Propionate', y='Simpson', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'),
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Propionate Concentration [mg/ml]', ylab = 'Simpson-Index') +
facet_wrap(~Time)
ggscatter(SCFA_stool, x='Total.SCFA', y='Simpson', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'),
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Total SCFA Concentration [mg/ml]', ylab = 'Simpson-Index') +
facet_wrap(~Time)
ggscatter(SCFA_stool, x='Total.SCFA', y='Simpson',
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Total SCFA Concentration [µmol/g]', ylab = 'Simpson-Index')
Daten sichern
corr_aphadiv_SCFA <- data_frame()
corr_aphadiv_SCFA <-bind_rows(corr_spearman_Shannon_SCFA,corr_spearman_Simpson_SCFA)
write.table(corr_aphadiv_SCFA, file = "/Users/student05/Documents/SCFA/SCFA analyse/OTU SCFA analyse/corr_alphadiv_SCFA.txt", sep= "\t", col.names = TRUE, row.names = FALSE)
Shannon and Simpson-Index je Time Point und “high concentration” Probands
SCFA_stool_con <- read.table("/Users/student05/Documents/SCFA/SCFA Tabelle Phenotypen.txt", sep = '\t', comment='',head=T, row.names = 1)
common.ids.St <- intersect(rownames(SCFA_stool_con), rownames(map_alphadiv))
common.ids.St <- intersect(row.names(SCFA_stool_con), row.names(map_alphadiv))
SCFA_stool_con <- SCFA_stool_con[common.ids.St,]
map_alphadiv <- map_alphadiv[common.ids.St,]
Erstellen einer Liste fuer comparisons in boxplots
comparison_con <- list(c("high concentrations", "normal concentrations"))
Wilcoxon Test zwischen Phaenotypen und Shannon + Boxplot
pairwise.wilcox.test(subset(filter(SCFA_stool_con, Time == "PRE"))$Shannon, subset(filter(SCFA_stool_con, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(SCFA_stool_con)), aes(x=Phenotype, y=Shannon)) + xlab('Phenotype') + ylab('Shannon-Index')+
geom_boxplot(fill ='whitesmoke', color = 'black') +
geom_dotplot(binaxis = ' y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))
Wilcoxon test zwischen Phaenotypen und Simpson + Boxplot
pairwise.wilcox.test(subset(filter(SCFA_stool_con, Time == "PRE"))$Simpson, subset(filter(SCFA_stool_con, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(SCFA_stool_con)), aes(x=Phenotype, y=Simpson)) + xlab('Phenotype') + ylab('Simpson-Index')+
geom_boxplot(fill ='whitesmoke', color = 'black') +
geom_dotplot(binaxis = ' y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(method = "wilcox.test",comparisons = comparison_con, paired = FALSE, aes(label = ..p.signif..))
1.6 Relative Abundance SCFA-Analyse Laden, filtern fuer high abundant taxa und sichern der Metadaten
L6_rarefied <- read.table("/Users/student05/Documents/Mappingfile_16SrRNA_BC22_L6.txt", sep= '\t', comment='', head=T)
L6_rarefied <- filter(L6_rarefied, Bodysite == "Stool")
row.names(L6_rarefied) <- L6_rarefied$X.SampleID
L6_rarefied <- L6_rarefied[,-c(1:18)]
L6_taxa <- L6_rarefied[, colSums(L6_rarefied > 0.01) > 10]
L6_taxa <- L6_taxa %>% select(-starts_with("Unassigned"))
L6_taxa<- sweep(L6_taxa, 1, rowSums(L6_taxa),'/')
map_KD <- read.table("/Users/student05/Documents/Mappingfile_16SrRNA_BC22.txt", sep ='\t', comment='', head=T,
row.names = 1)
L6_taxa <- rownames_to_column(L6_taxa, "SampleID")
map_KD <- rownames_to_column(map_KD, "SampleID")
L6_metadata_taxa <- merge(map_KD, L6_taxa, by.x=c("SampleID"), by.y=c("SampleID"))
L6_metadata_taxa <- L6_metadata_taxa[,-c(2,3,10:15)]
L6_metadata_taxa <- L6_metadata_taxa[,-c(9)]
write.table(L6_metadata_taxa, file = '/Users/student05/Documents/relative abundance/L6_metadata_taxa_strict_stool.txt', sep = "\t", col.names = TRUE,row.names = FALSE)
Filtern des Datensatzes mit der realtive abundance nach den Zeitpunkten, Bestimmung der Means je Zeitpunkt Zusammenfuegen der Datensätze
relab <- read.table("/Users/student05/Documents/relative abundance/L6_metadata_taxa_strict_stool.txt", sep = '\t', comment='', head=T)
relab_PRE <- filter(relab, Time == "PRE")
relab_POST <- filter(relab, Time == "POST")
relab_FU <- filter(relab, Time == "FOLLOW-UP")
relab_means_PRE <- aggregate(relab_PRE[, 10:90], list(relab_PRE$Proband), mean)
relab_means_PRE['Time'] = 'PRE'
relab_means_PRE <- rename(relab_means_PRE, Proband=Group.1)
relab_means_POST <- aggregate(relab_POST[, 10:90], list(relab_POST$Proband), mean)
relab_means_POST['Time'] = 'POST'
relab_means_POST <- rename(relab_means_POST, Proband=Group.1)
relab_means_FU <- aggregate(relab_FU[, 10:90], list(relab_FU$Proband), mean)
relab_means_FU['Time'] = 'FOLLOW-UP'
relab_means_FU <- rename(relab_means_FU, Proband=Group.1)
relab_means <- data_frame()
relab_means <- bind_rows(relab_means_PRE, relab_means_POST, relab_means_FU)
relab_means <- relab_means[, c(1, 83, 2:82)]
ncol(relab_means)
write.table(relab_means, file = '/Users/student05/Documents/relative abundance/relab_means_per_timepoint.txt',sep = "\t", col.names = TRUE, row.names = FALSE)
Umbenennen der Spalten
relab_means <- read.table('/Users/student05/Documents/relative abundance/relab_means_per_timepoint.txt', sep ='\t', comment='', head=T)
relab_means_melt <- melt(relab_means, id=c('Proband', 'Time'))
relab_means_melt <- rename(relab_means_melt, Taxa=variable)
relab_means_melt <- rename(relab_means_melt, Relative_Abundance=value)
Subset phylum und genus level, sichern der Daten
relab_phylum <- subset(relab_means_melt, !grepl("g__|f__|o__|c__", relab_means_melt$Taxa))
relab_phylum <- subset(relab_phylum, !grepl("k__Archaea", relab_phylum$Taxa))
relab_phylum$Time <- factor(relab_phylum$Time, levels=c('PRE','POST','FOLLOW-UP'))
relab_phylum_spread <- spread(relab_phylum, Taxa, Relative_Abundance, sep = NULL)
relab_genus <- subset(relab_means_melt, grepl("g__", relab_means_melt$Taxa))
relab_genus <- subset(relab_genus, !grepl("k__Archaea", relab_genus$Taxa))
relab_genus$Time <- factor(relab_genus$Time, levels = c('PRE','POST','FOLLOW-UP'))
relab_genus_spread <- spread(relab_genus, Taxa, Relative_Abundance, sep = NULL)
write.table(relab_phylum_spread, file = '/Users/student05/Documents/relative abundance/relab_phylum.txt', sep= "\t", col.names = TRUE, row.names = FALSE)
write.table(relab_genus_spread, file = '/Users/student05/Documents/relative abundance/relab_genus.txt', sep ="\t", col.names = TRUE, row.names = FALSE)
Testen der Taxa auf Normalverteilung, Phylum und Genus Anschließendes Filtern nach Normalverteilung
test_normdist_phylum <- data.frame()
phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])
for (i in phylum_colnames) {
fit <- shapiro.test(relab_phylum_spread[,i])
p = fit$p.value
nrow = nrow(test_normdist_phylum)+1
test_normdist_phylum[nrow, "p.value"] = p
test_normdist_phylum[nrow, "column"] = i
}
test_normdist_genus <- data_frame()
genus_colnames <-colnames(relab_genus_spread[, c(3:31)])
for (i in genus_colnames) {
fit <- shapiro.test(relab_genus_spread[,i])
p = fit$p.value
nrow = nrow(test_normdist_genus)+1
test_normdist_genus[nrow, "p.value"] = p
test_normdist_genus[nrow, "column"] = i
}
normdist_phylum <- filter(test_normdist_phylum, p.value > 0.05)
normdist_genus <- filter(test_normdist_genus, p.value > 0.05)
-> nur Bacteroidetes, Bacteroides, Dorea, Blautia, Faecalibacterium normalverteilt
Korrelationsanalysen mit SCFA
Synchonisieren der Metadaten
relab_phylum_ID <- relab_phylum_spread
relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))
row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID
relab_genus_ID <- relab_genus_spread
relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))
row.names(relab_genus_ID) <- relab_genus_ID$SampleID
SCFA_stool <- read.table("/Users/student05/Downloads/SCFA_stool total SCFA.txt", sep = '\t', comment='',head=T, row.names = 1)
View(SCFA_stool)
SCFA_stool<- add.rownames(SCFA_stool, "SampleID")
SCFA_stool$Time <-factor(SCFA_stool$Time, levels = c("PRE", "POST", "FOLLOWUP"))
SCFA_stool[1,3]<- "PRE"
SCFA_stool[1,4]<- "OU1"
SCFA_stool <- mutate(SCFA_stool, SampleID1 = paste(Proband, Time, sep = "."))
row.names(SCFA_stool) <- SCFA_stool$SampleID1
common.ids.relab <- intersect(rownames(SCFA_stool), rownames(relab_phylum_ID))
SCFA_stool <- SCFA_stool[common.ids.relab,]
relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
write.table(SCFA_stool, file = '/Users/student05/Documents/SCFA/SCFA_stool_total.txt', sep= "\t", col.names = TRUE, row.names = FALSE)
Erstellen einer Matrix zum testen der gewuenschten Daten, hinzufuegen von einem Pseudocount 0.00001 und log-Transformation
phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])
relab_phylum_ID_log <- relab_phylum_ID[,c(3:8)] + 0.1
relab_phylum_ID_log <- log10(relab_phylum_ID_log)
phylum_SCFA <- cbind(relab_phylum_ID_log, SCFA_stool[, c(1, 3:5, 7:11)])
phylum_SCFA$Time <- factor(phylum_SCFA$Time, levels = c("PRE", "POST"))
Loop Korrelationsanalyse Acetat und Phylum-level
corr_map_phylum_Ac <- filter(phylum_SCFA, !is.na(Acetate))
corr_spearman_Phylum_Ac <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Ac, !is.na(i))
y = tmp[,i]
x = tmp$Acetate
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Acetate
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Acetate
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Ac)+1
corr_spearman_Phylum_Ac[nrow,"SCFA"] <- "Acetate"
corr_spearman_Phylum_Ac[nrow, "Phylum"] = i
corr_spearman_Phylum_Ac[nrow, "p.value"] = p
corr_spearman_Phylum_Ac[nrow, "rho"] = rho
corr_spearman_Phylum_Ac[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Ac[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Ac[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Ac[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Ac$p.adjusted <- p.adjust(corr_spearman_Phylum_Ac$p.value, method = "BH", n = 35)
corr_spearman_Phylum_Ac$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Ac$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Ac$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Ac$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_Ac <- filter(corr_spearman_Phylum_Ac, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_Ac, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.Ac.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von Acetat und phylum-level
phylum_SCFA$Time <- factor(phylum_SCFA$Time, levels = c("PRE", "POST"))
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time, scales = "free_x")
ggscatter(phylum_SCFA, x='Acetate', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance p__Bacteroidetes')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_SCFA, x='Acetate', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance p__Bacteroidetes')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)
ggscatter(phylum_SCFA, x='Acetate', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_SCFA, x='Acetate', y='k__Bacteria.p__Verrucomicrobia', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
Loop Korrelationsanalyse Propionat und Phylum-level
corr_map_phylum_Pr <- filter(phylum_SCFA, !is.na(Propionate))
corr_spearman_Phylum_Pr <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Pr, !is.na(i))
y = tmp[,i]
x = tmp$Propionate
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Propionate
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Propionate
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Pr)+1
corr_spearman_Phylum_Pr[nrow,"SCFA"] <- "Propionate"
corr_spearman_Phylum_Pr[nrow, "Phylum"] = i
corr_spearman_Phylum_Pr[nrow, "p.value"] = p
corr_spearman_Phylum_Pr[nrow, "rho"] = rho
corr_spearman_Phylum_Pr[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Pr[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Pr[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Pr[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Pr$p.adjusted <- p.adjust(corr_spearman_Phylum_Pr$p.value, method = "BH", n = 35)
corr_spearman_Phylum_Pr$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Pr$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Pr$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Pr$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_Pr <- filter(corr_spearman_Phylum_Pr, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_Pr, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.Pr.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Propionat und phylum-level
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)
Loop Butyrat und Phylum-level
corr_map_phylum_Bu <- filter(phylum_SCFA, !is.na(Butyrate))
corr_spearman_Phylum_Bu <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Bu, !is.na(i))
y = tmp[,i]
x = tmp$Butyrate
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Butyrate
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Butyrate
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Bu)+1
corr_spearman_Phylum_Bu[nrow,"SCFA"] <- "Butyrate"
corr_spearman_Phylum_Bu[nrow, "Phylum"] = i
corr_spearman_Phylum_Bu[nrow, "p.value"] = p
corr_spearman_Phylum_Bu[nrow, "rho"] = rho
corr_spearman_Phylum_Bu[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Bu[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Bu[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Bu[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Bu$p.adjusted <- p.adjust(corr_spearman_Phylum_Bu$p.value, method = "BH", n = 35)
corr_spearman_Phylum_Bu$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Bu$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Bu$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Bu$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_Bu <- filter(corr_spearman_Phylum_Bu, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_Bu, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.Bu.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Butyrate und phylum-level
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
scale_facet_wrap_discrete(limits = c("PRE", "POST"))
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)
Loop fuer Isobutyrat und Phylum-level
corr_map_phylum_IB <- filter(phylum_SCFA, !is.na(Iso.Butyrate))
corr_spearman_Phylum_IB <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_IB, !is.na(i))
y = tmp[,i]
x = tmp$Iso.Butyrate
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Iso.Butyrate
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Iso.Butyrate
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_IB)+1
corr_spearman_Phylum_IB[nrow,"SCFA"] <- "Iso.Butyrate"
corr_spearman_Phylum_IB[nrow, "Phylum"] = i
corr_spearman_Phylum_IB[nrow, "p.value"] = p
corr_spearman_Phylum_IB[nrow, "rho"] = rho
corr_spearman_Phylum_IB[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_IB[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_IB[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_IB[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_IB$p.adjusted <- p.adjust(corr_spearman_Phylum_IB$p.value, method = "BH", n = 35)
corr_spearman_Phylum_IB$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_IB$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_IB$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_IB$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_IB <- filter(corr_spearman_Phylum_IB, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_IB, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.IB.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Isobutyrat und Phylum-level
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)
Loop Total SCFA und Phylum-level
corr_map_phylum_TS <- filter(phylum_SCFA, !is.na(Total.SCFA))
corr_spearman_Phylum_TS <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_TS, !is.na(i))
y = tmp[,i]
x = tmp$Total.SCFA
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Total.SCFA
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Total.SCFA
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_TS)+1
corr_spearman_Phylum_TS[nrow,"SCFA"] <- "Total.SCFA"
corr_spearman_Phylum_TS[nrow, "Phylum"] = i
corr_spearman_Phylum_TS[nrow, "p.value"] = p
corr_spearman_Phylum_TS[nrow, "rho"] = rho
corr_spearman_Phylum_TS[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_TS[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_TS[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_TS[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_TS$p.adjusted <- p.adjust(corr_spearman_Phylum_TS$p.value, method = "BH", n = 35)
corr_spearman_Phylum_TS$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_TS$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_TS$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_TS$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_TS <- filter(corr_spearman_Phylum_TS, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_TS, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.TS.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Total SCFA und phylum-level
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)
Sichern der Daten
corr_phylum_SCFA <- data_frame()
corr_phylum_SCFA <- bind_rows(corr_spearman_Phylum_Ac,corr_spearman_Phylum_Pr,corr_spearman_Phylum_Bu, corr_spearman_Phylum_IB, corr_spearman_Phylum_TS)
write.table(corr_phylum_SCFA, file = '/Users/student05/Documents/relative abundance/corr_phylum_SCFA_all_PRE_POST.txt',sep = "\t", col.names = TRUE, row.names = FALSE)
Analysen mit Genus-Level
Erstellen und filtern der Matrix, Log-Transformation und hinzufuegen von Pseudocount 0.00001
genus_colnames <- colnames(relab_genus_spread[, c(3:31)])
relab_genus_ID_log <- relab_genus_ID[,c(3:31)] + 0.00001
relab_genus_ID_log <- log10(relab_genus_ID_log)
genus_SCFA <- cbind(relab_genus_ID_log, SCFA_stool[, c(1:10)])
Loop Acetate und Genus-Level
corr_map_genus_Ac <- filter(genus_SCFA, !is.na(Acetate))
corr_spearman_genus_Ac <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Ac, !is.na(i))
y = tmp[,i]
x = tmp$Acetate
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Acetate
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Acetate
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Ac)+1
corr_spearman_genus_Ac[nrow,"SCFA"] = "Acetate"
corr_spearman_genus_Ac[nrow, "Genus"] = i
corr_spearman_genus_Ac[nrow, "p.value"] = p
corr_spearman_genus_Ac[nrow, "rho"] = rho
corr_spearman_genus_Ac[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Ac[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Ac[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Ac[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Ac$p.adjusted <- p.adjust(corr_spearman_genus_Ac$p.value, method = "BH", n = 35)
corr_spearman_genus_Ac$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Ac$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Ac$p.adjusted_POST <- p.adjust(corr_spearman_genus_Ac$p.value_POST, method = "BH", n = 35)
corr_sig_genus_Ac <- filter(corr_spearman_genus_Ac, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_Ac, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.Ac.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Acetate und genus-level
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time)
ggscatter(genus_SCFA, x='Acetate', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -2), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_SCFA, x='Acetate', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -2), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance g__Oscillospira')+theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [nmol/mg DW]') +
ylab('log10 (Relative Abundance g__Collinsella)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__Faecalibacterium )')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') +
ylab('log10 (Relative Abundance g__Akkermansia )')+
facet_wrap(~Time)
ggscatter(genus_SCFA, x='Acetate', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -1.1), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_SCFA, x='Acetate', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -1.1), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__Collinsella )')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=Acetate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__Bacteroides )')+
facet_wrap(~Time)
Loop Propionat und Genus-Level
corr_map_genus_Pr <- filter(genus_SCFA, !is.na(Propionate))
corr_spearman_genus_Pr <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Pr, !is.na(i))
y = tmp[,i]
x = tmp$Propionate
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Propionate
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Propionate
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Pr)+1
corr_spearman_genus_Pr[nrow,"SCFA"] = "Propionate"
corr_spearman_genus_Pr[nrow, "Genus"] = i
corr_spearman_genus_Pr[nrow, "p.value"] = p
corr_spearman_genus_Pr[nrow, "rho"] = rho
corr_spearman_genus_Pr[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Pr[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Pr[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Pr[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Pr$p.adjusted <- p.adjust(corr_spearman_genus_Pr$p.value, method = "BH", n = 35)
corr_spearman_genus_Pr$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Pr$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Pr$p.adjusted_POST <- p.adjust(corr_spearman_genus_Pr$p.value_POST, method = "BH", n = 35)
corr_sig_genus_Pr <- filter(corr_spearman_genus_Pr, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_Pr, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.Pr.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Propionat und genus-level
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=Propionate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__Akkermansia)')+
facet_wrap(~Time)
Loop Butyrat und Genus-Level
corr_map_genus_Bu <- filter(genus_SCFA, !is.na(Butyrate))
corr_spearman_genus_Bu <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Bu, !is.na(i))
y = tmp[,i]
x = tmp$Butyrate
tmp_corr_spearman <- cor.test(x, y, method="spearman", paied = T)
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Butyrate
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman", paied = T)
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Butyrate
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman", paied = T)
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Bu)+1
corr_spearman_genus_Bu[nrow,"SCFA"] = "Butyrate"
corr_spearman_genus_Bu[nrow, "Genus"] = i
corr_spearman_genus_Bu[nrow, "p.value"] = p
corr_spearman_genus_Bu[nrow, "rho"] = rho
corr_spearman_genus_Bu[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Bu[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Bu[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Bu[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Bu$p.adjusted <- p.adjust(corr_spearman_genus_Bu$p.value, method = "BH", n = 35)
corr_spearman_genus_Bu$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Bu$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Bu$p.adjusted_POST <- p.adjust(corr_spearman_genus_Bu$p.value_POST, method = "BH", n = 35)
corr_sig_genus_Bu <- filter(corr_spearman_genus_Bu, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_Bu, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.Bu.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Butyrat und genus-level
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus., x=Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__.Ruminococcus)')+
facet_wrap(~Time)
Loop Isobutyrat und Genus-Level
corr_map_genus_BI <- filter(genus_SCFA, !is.na(Iso.Butyrate))
corr_spearman_genus_BI <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_BI, !is.na(i))
y = tmp[,i]
x = tmp$Iso.Butyrate
tmp_corr_spearman <- cor.test(x, y, method="spearman", paired = T)
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Iso.Butyrate
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman", paired = T)
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Iso.Butyrate
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman", paied = T)
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_BI)+1
corr_spearman_genus_BI[nrow,"SCFA"] = "Iso.Butyrate"
corr_spearman_genus_BI[nrow, "Genus"] = i
corr_spearman_genus_BI[nrow, "p.value"] = p
corr_spearman_genus_BI[nrow, "rho"] = rho
corr_spearman_genus_BI[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_BI[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_BI[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_BI[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_BI$p.adjusted <- p.adjust(corr_spearman_genus_BI$p.value, method = "BH", n = 35)
corr_spearman_genus_BI$p.adjusted_PRE <- p.adjust(corr_spearman_genus_BI$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_BI$p.adjusted_POST <- p.adjust(corr_spearman_genus_BI$p.value_POST, method = "BH", n = 35)
corr_sig_genus_BI <- filter(corr_spearman_genus_BI, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_BI, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.BI.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Isobutyrat genus-level
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance f__Coriobacteriaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__RF32.f__.g__, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance c__Alphaproteobacteria.o__RF32)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__.g__, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance c__Clostridia.o__Clostridiales)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus, x=Iso.Butyrate)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') +
ylab('log10 (Relative Abundance g__Coprococcus)')+
facet_wrap(~Time)
Loop total SCFA und Genus-Level
corr_map_genus_TS <- filter(genus_SCFA, !is.na(Total.SCFA))
corr_spearman_genus_TS <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_TS, !is.na(i))
y = tmp[,i]
x = tmp$Total.SCFA
tmp_corr_spearman <- cor.test(x, y, method="spearman", paired = T)
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Total.SCFA
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman", paired = T)
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Total.SCFA
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman", paired = T)
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_TS)+1
corr_spearman_genus_TS[nrow,"SCFA"] = "Total.SCFA"
corr_spearman_genus_TS[nrow, "Genus"] = i
corr_spearman_genus_TS[nrow, "p.value"] = p
corr_spearman_genus_TS[nrow, "rho"] = rho
corr_spearman_genus_TS[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_TS[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_TS[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_TS[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_TS$p.adjusted <- p.adjust(corr_spearman_genus_TS$p.value, method = "BH", n = 35)
corr_spearman_genus_TS$p.adjusted_PRE <- p.adjust(corr_spearman_genus_TS$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_TS$p.adjusted_POST <- p.adjust(corr_spearman_genus_TS$p.value_POST, method = "BH", n = 35)
corr_sig_genus_TS <- filter(corr_spearman_genus_TS, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_TS, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.TS.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten total SCFA und genus-level
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') +
ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=Total.SCFA)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') +
ylab('log10 (Relative Abundance g__Akkermansia)')+
facet_wrap(~Time)
Daten sichern
corr_genus_SCFA <- data_frame()
corr_genus_SCFA <- bind_rows(corr_spearman_genus_Ac, corr_spearman_genus_Pr,corr_spearman_genus_Bu, corr_spearman_genus_BI, corr_spearman_genus_TS)
write.table(corr_genus_SCFA, file = '/Users/student05/Documents/relative abundance/corr_genus_SCFA_all_PRE_POST.txt', sep = "\t", col.names = TRUE, row.names = FALSE)
corr_genus_SCFA_sig <- data_frame()
corr_genus_SCFA_sig <- bind_rows(corr_sig_genus_Ac, corr_sig_genus_Pr, corr_sig_genus_Bu, corr_sig_genus_BI, corr_sig_genus_TS)
write.table(corr_genus_SCFA_sig, file ='/Users/student05/Documents/relative abundance/corr_genus_SCFA_sig_PRE_POST.txt',sep ="\t", col.names = TRUE, row.names = FALSE)
1.7 Testen von Unterschieden im relativen Vorkommen der Taxa von Probanden mit sehr hohen SCFA-Konzentrationen und normalen SCFA-Konzentrationen
Laden der Metadaten
SCFA_Pt <- read.table("/Users/student05/Documents/SCFA/SCFA Tabelle Phenotypen.txt", sep ='\t',comment='',head=T)
SCFA_Pt[ ,6] <- NULL
Synchonisieren der Metadaten
SCFA_Pt <- mutate(SCFA_Pt, SampleID1 = paste(Proband, Time, sep = "."))
row.names(SCFA_Pt) <- SCFA_Pt$SampleID1
common.ids.relab <- intersect(rownames(SCFA_Pt), rownames(relab_phylum_ID))
SCFA_Pt <- SCFA_Pt[common.ids.relab,]
relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
Matrix erstellen, filtern, hinzufuegen von log und Psedocount
write.table(SCFA_stool, file = '/Users/student05/Documents/SCFA/SCFA_Pt.txt', sep= "\t", col.names = TRUE, row.names = FALSE)
relab_phylum_ID_log <- relab_phylum_ID[,c(3:8)] + 0.1
relab_phylum_ID_log <- log10(relab_phylum_ID_log)
phylum_Pt <- cbind(relab_phylum_ID_log, SCFA_Pt[, c(1, 3:5, 7:11, 14)])
Phylum-Differenzen zwsichen den Phaenotypen
comparison_con <- list(c("low concentrations", "high concentrations"))
pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Firmicutes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Firmicutes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)
phylum_Pt$k__Bacteria.p__Actinobacteria
pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Actinobacteria, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Actinobacteria)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Actinobacteria)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)
phylum_Pt$k__Bacteria.p__Bacteroidetes
pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Bacteroidetes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)
pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Proteobacteria, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Proteobacteria)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Proteobacteria)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)
pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Tenericutes, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Tenericutes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Tenericutes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)
pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Verrucomicrobia)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Verrucomicrobia)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)
1.8 Beta-Diversitaet
Laden und filtern der Metadaten
otus_means <- read.table("/Users/student05/Documents/otus_means_stool 2.txt", sep ='\t',comment='',head=T)
setwd("/Users/student05/Documents/SCFA")
map_bdiv <- read.table("/Users/student05/Documents/Mappingfile_16SrRNA_BC22.txt", sep ='\t',comment='',head=T)
map_bdiv <- mutate(map_bdiv, SampleID2 = paste(Proband, Time, sep = "."))
map_bdiv <- filter(map_bdiv, Timepoint == "0U1" | Timepoint =="0U2" | Timepoint =="0U3")
map_bdiv <- filter(map_bdiv, Bodysite == "Stool")
rownames(map_bdiv) <- map_bdiv$SampleID2
Synchonisieren der Daten
otus_relab <- mutate(otus_means, SampleID = paste(Proband, Time, sep = "."))
rownames(otus_relab) <- otus_relab$SampleID
otus_relab <- sweep(otus_relab[, 2:84606], 1, rowSums(otus_relab[, 2:84606]), '/')
dim(otus_relab)
common.ids.relab <- intersect(rownames(map_bdiv), rownames(otus_relab))
map_bdiv <- map_bdiv[common.ids.relab,]
otus_relab <- otus_relab[common.ids.relab,]
dim(otus_relab)
write.table(otus_relab, file = "otus_relab_bc.txt", sep = "\t", col.names = TRUE,row.names = TRUE)
Bray-Curtis Kalkulation
d.bray <-vegdist(otus_relab)
matrix.bray<- as.matrix(d.bray)
write.table(matrix.bray, file = "matrix.bray.txt", sep = "\t", col.names = T, row.names = T)
Erstellen einer Distance-matrix (All functions used are created by Daniel Podlesny and saved in the R Script “modify_distmat.R”. Open the script in R and run all functions. They will then be listed in the Environment. call the function with the distance matrix for this project)
distmat <- clean_distmat(as.data.frame(matrix.bray))
distmat <- distmat_to_long(distmat, rm_diag = TRUE)
distmat_PRE <- filter(distmat, grepl('*.PRE', distmat$row) & grepl('*.PRE', distmat$col))
distmat_POST <- filter(distmat, grepl('*.POST', distmat$row) & grepl('*.POST', distmat$col))
distmat_FU <- filter(distmat, grepl('*.FOLLOW-UP', distmat$row) & grepl('*.FOLLOW-UP', distmat$col))
distmat_PRE['Time'] = 'PRE'
distmat_POST['Time'] = 'POST'
distmat_FU['Time'] = 'FOLLOW-UP'
distmat_PREvsPOST <- data_frame()
distmat_PREvsPOST <- bind_rows(distmat_PRE, distmat_POST)
distmat_PREvsPOST$Time <- factor(distmat_PREvsPOST$Time, levels = c("PRE", "POST"))
distmat_all <- data_frame()
distmat_all <- bind_rows(distmat_PRE, distmat_POST, distmat_FU)
distmat_all$Time <- factor(distmat_all$Time, levels=c("PRE", "POST", "FOLLOW-UP"))
Filtern fuer Proben mit PRE und POST
distmat_PREvsPOST_pairs <- filter(distmat_PREvsPOST, !row == "31KE.POST" & !row =="34WF.PRE" & !row == "45GL.POST" & !row == "49RJ.PRE" &!row == "54SL.POST" & !row == "70PL.PRE" & !row == "74SA.POST")
distmat_PREvsPOST_pairs <- filter(distmat_PREvsPOST_pairs, !col == "31KE.POST" &!col == "34WF.PRE" & !col == "45GL.POST" & !col == "49RJ.PRE" & !col == "54SL.POST" & !col == "70PL.PRE" & !col == "74SA.POST")
Wilcoxon-Test PRE und POST + boxplot
pairwise.wilcox.test(distmat_PREvsPOST_pairs$distance, distmat_PREvsPOST_pairs$Time, p.adjust.method = "BH", paired = TRUE)
pairwise.wilcox.test(distmat_PREvsPOST_pairs$distance, distmat_PREvsPOST_pairs$Time, p.adjust.method = "BH", paired = TRUE)
ggplot(distmat_PREvsPOST_pairs, aes(x=Time, y=distance)) + xlab('Timepoint') + ylab('Bray-Curtis Dissimilarity (baseline per proband)') +
geom_boxplot(fill='whitesmoke', color="black") + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.2) +
ggtitle('Beta-Diversity between probands before and after a 6-week Ketogenic Diet') +
stat_compare_means(comparison = list(c("PRE", "POST")), paired = TRUE, aes(label= ..p.signif..))
Mean und SD für PRE und POST
mean(subset(filter(distmat_PREvsPOST_pairs, Time == "PRE"))$distance)
sd(subset(filter(distmat_PREvsPOST_pairs, Time == "PRE"))$distance)
mean(subset(filter(distmat_PREvsPOST_pairs, Time == "POST"))$distance)
sd(subset(filter(distmat_PREvsPOST_pairs, Time == "POST"))$distance)
- FA-Analyse
2.1 Normalverteilung Metadaten Laden, filtern und sortieren
FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
head=T)
View(FA_stool)
FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST"))
FA_stool <- FA_stool[-c(58:64),]
FA_stool<- add_rownames(FA_stool, "SampleID1")
FA_stool.r<- add_rownames(FA_stool, "SampleID1")
row.names(FA_stool) <- FA_stool$SampleID
Testen auf Normalverteilung
FA_colnames <- colnames(FA_stool[, c(7:19)])
nd.FA<- data_frame()
for (i in FA_colnames) {
fit <- shapiro.test(as.matrix(as.data.frame(lapply(FA_stool[,i],
as.numeric))))
p = fit$p.value
nrow = nrow(nd.FA)+1
nd.FA[nrow, "column"] = i
nd.FA[nrow, "p.value"] = round(p, 4)
}
sign.nd_FA <- filter(nd.FA, p.value > 0.05)
Plotten der Normalverteilungen
ggqqplot(FA_stool$sat, ylab = "Saturated FA concentration nmol/g", xlab = "SampleID")
ggqqplot(FA_stool$mono.unsat, ylab = "Mono unsaturated concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$di.unsat, ylab = "Diunsaturated concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$less.14, ylab = " < 14 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$c18.19, ylab = "FA with 18-19 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$c14.17, ylab = "FA with 14-17 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$c20.21, ylab = "FA with 20-21 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$c22.24, ylab = "FA with 22-24 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$total, ylab = "Total FA concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool.r$n.3, ylab = "Omega 3 FA concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool.r$n.6, ylab = "Omega 6 FA concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool.r$ratio6.3, ylab = "Omega 6/3 ratio [nmol/g]", xlab = "SampleID")
Filtern nach PRE und POST Proben
FA_stool_pairs <- filter(FA_stool, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
FA_stool_pairs_PP <- filter(FA_stool_pairs, Time=="PRE" | Time=="POST")
FA_stool_pairs_PPFU <- filter(FA_stool, Proband == "05AP" | Proband == "13BS" | Proband == "17SK" | Proband == "22WS" | Proband == "40WA" | Proband == "41ML" | Proband == "54SL")
Wilcoxon-Test zwischen den Zeitpunkten PRE und POST Erstellen eines neuen Dataframes
wilcox_FA<- data_frame()
environment(filter)
for (i in FA_colnames) {
tmp <- FA_stool_pairs %>% drop_na(i)
x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y <- FA_stool_pairs$Time
tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = T)
p <- tmp_wilcox$p.value
nrow = nrow(wilcox_FA)+1
wilcox_FA[nrow, "LI"] <- i
wilcox_FA[nrow, "Mean PRE"] <-round(mean(subset(filter(FA_stool_pairs,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
wilcox_FA[nrow, "sd PRE"] <-round(sd(c(subset(filter(FA_stool_pairs,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), na.rm = TRUE)), 4)
wilcox_FA[nrow, "Mean POST"] <-round(mean(subset(filter(FA_stool_pairs,Time == "POST")[,i],!is.na(i), na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
wilcox_FA[nrow, "sd POST"] <- round(sd(c(subset(filter(FA_stool_pairs,Time == "POST")[,i],!is.na(i), na.rm = TRUE),na.rm = TRUE)), 4)
wilcox_FA[nrow, "p.value"] <- round(p, 4) }
write.table(wilcox_FA, file = '/Users/student05/Documents/fa feces/fa tabellen/FA.pre.post.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Zeitpunkte zusammen
wilcox_FA1<- data_frame()
for (i in FA_colnames) {
tmp <- FA_stool_pairs %>% drop_na(i)
x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y <- FA_stool_pairs$Time
tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = T)
p <- tmp_wilcox$p.value
nrow = nrow(wilcox_FA1)+1
wilcox_FA1[nrow, "LI"] <- i
wilcox_FA1[nrow, "Mean"] <-round(mean(subset(filter(FA_stool_pairs)[,i],!is.na(i),na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
wilcox_FA1[nrow, "sd"] <-round(sd(c(subset(filter(FA_stool_pairs)[,i],!is.na(i),na.rm = TRUE), na.rm = TRUE)), 4)
wilcox_FA1[nrow, "p.value"] <- round(p, 4) }
write.table(wilcox_FA1, file = '/Users/student05/Documents/fa feces/fa tabellen/FA.alltimes.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Boxplot der FA je Zeitpunkt
Melt Daten
FA_stool.melt <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('sat', 'mono.unsat', 'di.unsat', 'more.2.unsat', 'less.14', 'c14.17', 'c18', 'c20.24', 'total'))
FA_stool.melt <- dplyr::rename(FA_stool.melt, FA=variable)
FA_stool.melt <- dplyr::rename(FA_stool.melt, Concentration=value)
ggplot(FA_stool.melt,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("saturated", "monounsaturated", "diunsaturated", "> 2 unsaturated", "< c14", "c 14-17", "c 18-19", "c 20-21", "c 22-24", "total", "iso", "anteiso"),
values = c("tomato", "yellowgreen", "steelblue2", "orchid2", "deeppink", "brown4", "darkorange1", "blueviolet", "aquamarine3", "darksalmon", "cyan3", "darkgreen")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
Plots, die in Arbeit vorkommen
nach Saettigung
FA_stool.melt.sat <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('sat', 'mono.unsat', 'di.unsat', 'more.2.unsat'))
FA_stool.melt.sat <- dplyr::rename(FA_stool.melt.sat, FA=variable)
FA_stool.melt.sat <- dplyr::rename(FA_stool.melt.sat, Concentration=value)
FA_stool.melt.sat$Time <- factor(FA_stool.melt.sat$Time, levels = c("PRE", "POST"))
pdf("/Users/student05/Documents/fertige Plots/FA.DB.KD.pdf",width=8, height=10)
ggplot(FA_stool.melt.sat,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/g]') +
geom_boxplot(width = .7, lwd=0.7) + theme_classic()+
scale_fill_manual(labels = c("0", "1", "2", "3-6"),
values = c("#f0f9e8", "#bae4bc", "#7bccc4", "#2b8cbe")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),axis.text=element_text(size=16))+
theme(legend.position="top")
dev.off()
MCTs
FA_stool.melt.mct <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('less.14'))
FA_stool.melt.mct <- dplyr::rename(FA_stool.melt.mct, FA=variable)
FA_stool.melt.mct <- dplyr::rename(FA_stool.melt.mct, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/FA.MCT.KD.pdf",width=8, height=10)
ggplot(FA_stool.melt.mct,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Zeitpunkt') + ylab ('Konzentrationen [nmol/g]') +
geom_boxplot(width = .7, lwd=0.7) +
scale_fill_manual(labels = c("MCT"),
values = c("navy")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),axis.text=element_text(size=16))+
theme(legend.position="top")
dev.off()
Kettenlaenge
FA_stool.melt.kl <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('less.14','c14.17', 'c18', 'c20.24'))
FA_stool.melt.kl$Time <- factor(FA_stool.melt.kl$Time, levels = c("PRE", "POST"))
FA_stool.melt.kl <- dplyr::rename(FA_stool.melt.kl, FA=variable)
FA_stool.melt.kl <- dplyr::rename(FA_stool.melt.kl, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/FA.KL.KD.pdf",width=8, height=10)
ggplot(FA_stool.melt.kl,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/g]') +
geom_boxplot(width = .7, lwd=0.7) + theme_classic()+
scale_fill_manual(labels = c("> 14 c (MCT)","c 14-17", "c 18", "c 20-24"),
values = c("#f1eef6", "#bdc9e1", "#74a9cf", "#0570b0")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),axis.text=element_text(size=16))+
theme(legend.position="top")
dev.off()
Total FA
FA_stool.melt.t <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('total'))
FA_stool.melt.t <- dplyr::rename(FA_stool.melt.t, FA=variable)
FA_stool.melt.t <- dplyr::rename(FA_stool.melt.t, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/FA.total.KD.pdf",width=6, height=10)
ggplot(FA_stool.melt.t,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/g]') +
geom_boxplot(width = .2, lwd=1) + theme_classic()+
scale_fill_manual(labels = c("Gesamtfettsäuren"),
values = c("cornflowerblue")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),axis.text=element_text(size=16))+
theme(legend.position="top")+
expand_limits(y=c(0, 3000))
dev.off()
Omega-FA
FA_stool.melt.o <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('Omega3', 'Omega6','ratio'))
FA_stool.melt.o <- dplyr::rename(FA_stool.melt.o, FA=variable)
FA_stool.melt.o <- dplyr::rename(FA_stool.melt.o, Concentration=value)
FA_stool.melt.o$Time <- factor(FA_stool.melt.o$Time, levels = c("PRE", "POST"))
pdf("/Users/student05/Documents/fertige Plots/FA.omega.KD.pdf",width=8, height=10)
ggplot(FA_stool.melt.o,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/g]') +
geom_boxplot(width = .6, lwd=0.7) + theme_classic()+
scale_fill_manual(labels = c("alpha-Linolensäure", "Linolsäure", "Omega 6/Omega 3 Verhältnis"),
values = c("#2c7fb8", "#7fcdbb", "#edf8b1")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),axis.text=element_text(size=16))+
theme(legend.position="top")+
expand_limits(y=c(0, 3000))
dev.off()
In Arbeit Korrelation zwischen Saettigung der Fettsaeuren und Konzentration
FA_stool.melt.kl$chain.length <- as.integer(FA_stool.melt.kl$chain.length)
FA_stool.melt.kl <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c( 'less.14', 'c14.17', 'c18'))
FA_stool.melt.kl.pre <- subset(filter(FA_stool.melt.kl, !Time =='POST'))
FA_stool.melt.kl <- dplyr::rename(FA_stool.melt.kl, chain.length=variable)
FA_stool.melt.kl <- dplyr::rename(FA_stool.melt.kl, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/KL.Konzentration.pdf",width=8, height=10)
ggscatter(FA_stool.melt.kl, x='chain.length', y='Concentration',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', conf.int = T,
cor.coef = T, cor.method = 'spearman',cor.coef.coord = c(1, 2500), cor.coef.size = 5, xlab= '..', ylab = 'Konzentration [nmol/g]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
dev.off()
pdf("/Users/student05/Documents/fertige Plots/KL.Konzentration2.pdf",width=8, height=10)
ggscatter(FA_stool.melt.kl, x='chain.length', y='Concentration', add = 'reg.line', color = "grey59",fill = "lightgray",conf.int = T,
cor.coef = T, cor.method = 'spearman',cor.coef.coord = c(1, 2500), cor.coef.size = 8, xlab= '..', ylab = 'Konzentration [nmol/g]')+
theme(strip.text.x = element_text(size = 20, colour = "black"))+
theme(text = element_text(size=20),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")+
geom_point(color='black', size=2.5)
dev.off()
cor.test(subset(filter(FA_stool.melt.kl))$chain.length, subset(filter(FA_stool.melt.kl))$Concentration, method = "spearman", exact = F)
p.adjust(c(2.2e-16), method = 'BH', n=1)
(S = 87355, p-value < 2.2e-16 alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.8619349 q-value =2.2e-16)
Boxplots einzelner FAs zu den Zeitpunkten PRE und POST
von gesaettigten FA bis Total FA
FA_stool.melt.sat <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('sat'))
FA_stool.melt.sat <- rename(FA_stool.melt.sat, FA=variable)
FA_stool.melt.sat <- rename(FA_stool.melt.sat, Concentration=value)
ggplot(FA_stool.melt.sat,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("saturated"),
values = c("tomato")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons = comparison_time)
FA_stool.melt.ms <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('mono.unsat'))
FA_stool.melt.ms <- rename(FA_stool.melt.ms, FA=variable)
FA_stool.melt.ms <- rename(FA_stool.melt.ms, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.ms,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("monounsaturated"),
values = c("yellowgreen")) +
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons = comparison_time)
FA_stool.melt.ds <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('di.unsat'))
FA_stool.melt.ds <- rename(FA_stool.melt.ds, FA=variable)
FA_stool.melt.ds <- rename(FA_stool.melt.ds, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.ds,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("diunsaturated"),
values = c("steelblue2")) +
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)
FA_stool.melt.m2u <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('more.2.unsat'))
FA_stool.melt.m2u <- rename(FA_stool.melt.m2u, FA=variable)
FA_stool.melt.m2u <- rename(FA_stool.melt.m2u, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.m2u,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("> 2 unsaturated"),
values = c("orchid2")) +
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)
FA_stool.melt.14 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('less.14'))
FA_stool.melt.14 <- rename(FA_stool.melt.14, FA=variable)
FA_stool.melt.14 <- rename(FA_stool.melt.14, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.14,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("< c14"),
values = c("deeppink")) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")
FA_stool.melt.1417 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('c14.17'))
FA_stool.melt.1417 <- rename(FA_stool.melt.1417, FA=variable)
FA_stool.melt.1417 <- rename(FA_stool.melt.1417, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.1417,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("c 14-17"),
values = c("brown4")) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)
FA_stool.melt.1417 <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('c14.17'))
FA_stool.melt.1417 <- rename(FA_stool.melt.1417, FA=variable)
FA_stool.melt.1417 <- rename(FA_stool.melt.1417, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.1417,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("c 14-17"),
values = c("brown4")) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")
FA_stool.melt.18 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('c18.19'))
FA_stool.melt.18 <- rename(FA_stool.melt.18, FA=variable)
FA_stool.melt.18 <- rename(FA_stool.melt.18, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.18,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("c 18-19"),
values = c("darkorange1")) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")
FA_stool.melt.20 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('c20.21'))
FA_stool.melt.20 <- rename(FA_stool.melt.20, FA=variable)
FA_stool.melt.20 <- rename(FA_stool.melt.20, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.20,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("c 20-21"),
values = c("blueviolet")) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")
FA_stool.melt.22 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('c22.24'))
FA_stool.melt.22 <- rename(FA_stool.melt.22, FA=variable)
FA_stool.melt.22 <- rename(FA_stool.melt.22, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.22,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("c 22-24"),
values = c("aquamarine3")) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")
FA_stool.melt.t <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('total'))
FA_stool.melt.t <- rename(FA_stool.melt.t, FA=variable)
FA_stool.melt.t <- rename(FA_stool.melt.t, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.t,aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ FA) +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("total"),
values = c("darksalmon")) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")
Korrelation zwischen Anzahl an Doppelbindungen und Konzentration der FA
Laden der Metadaten
FA_stool.db <- read.table("/Users/student05/Documents/DB sättigung .txt", sep = '\t', comment='',
head=T)
FA_stool.db$Time <- factor(FA_stool.db$Time, levels = c("PRE", "POST"))
Plotten der Korrelation In Arbeit
pdf("/Users/student05/Documents/fertige Plots/DB.2Konzentration.pdf",width=8, height=10)
ggscatter(FA_stool.db, x='DB', y='Concentration',color = "grey59",fill = "lightgray",shape = 19, add = 'reg.line', conf.int = T,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, 3000), cor.coef.size = 5, xlab= 'Anzahl an Doppelbindungen', ylab = 'Konzentration [nmol/g]')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")+
geom_point(color='black', size=2.5)
dev.off()
cor.test(subset(filter(FA_stool.db))$DB, subset(filter(FA_stool.db))$Concentration, method = "spearman", exact = F)
p.adjust(c(2.2e-16), method = 'BH', n=1)
(S = 3684500, p-value < 2.2e-16 alternative hypothesis: true rho is not equal to 0 sample estimates: rho -0.7034807)
2.2 Korrelationsanalysen zwischen den FAs mit Hilfe einer Korrelationsmatrix
Laden der Metadaten
FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
head=T)
FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST"))
FA_stool <- FA_stool[-c(58:64),]
FA_stool <- FA_
FA_stool<- add_rownames(FA_stool, "SampleID1")
FA_stool.r<- add_rownames(FA_stool, "SampleID1")
row.names(FA_stool) <- FA_stool$SampleID
Filtern nach PRE und POST Hinzufügen von Spearman
FA_stool_matrix_PRE <- subset(filter(FA_stool, Time == "PRE"))[ ,8:19]
FA_stool_matrix_POST <- subset(filter(FA_stool, Time == "POST"))[ ,8:19]
res.PRE <- cor(FA_stool_matrix_PRE)
res.POST <- cor(FA_stool_matrix_POST)
res2.PRE <- rcorr(as.matrix(FA_stool_matrix_PRE), type = "spearman")
res2.POST <- rcorr(as.matrix(FA_stool_matrix_POST), type = "spearman")
Bestimmung des Korrelationskoeffizienten
res2.PRE$r
res2.POST$r
FA_stool_PRE_CC <- as.matrix((res2.PRE$r))
FA_stool_POST_CC <- as.matrix(res2.POST$r)
Bestimmung p-values
res2$P
FA_stool_PRE_PV <- as.matrix(res2.PRE$P)
FA_stool_POST_PV <- as.matrix(res2.POST$P)
Erstellen einer flattenCorrMatrix für PRE und POST
flattenCorrMatrix.PRE <- function(FA_stool_PRE_CC, FA_stool_PRE_PV) {
ut <- upper.tri(FA_stool_PRE_CC)
data.frame(
row = rownames(FA_stool_PRE_CC)[row(FA_stool_PRE_CC)[ut]],
column = rownames(FA_stool_PRE_CC)[col(FA_stool_PRE_CC)[ut]],
cor =(FA_stool_PRE_CC)[ut],
p = FA_stool_PRE_PV[ut]
)
}
flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P)
flattenCorrMatrix.POST <- function(FA_stool_POST_CC, FA_stool_POST_PV) {
ut <- upper.tri(FA_stool_POST_CC)
data.frame(
row = rownames(FA_stool_POST_CC)[row(FA_stool_POST_CC)[ut]],
column = rownames(FA_stool_POST_CC)[col(FA_stool_POST_CC)[ut]],
cor =(FA_stool_POST_CC)[ut],
p = FA_stool_POST_PV[ut]
)
}
flattenCorrMatrix.POST(res2.POST$r, res2.POST$P)
Dataframe erstellen und Spalten umbenennen
FA_PRE_cor.p <- as.data.frame(flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P))
FA_POST_cor.p <- as.data.frame(flattenCorrMatrix.POST(res2.POST$r, res2.POST$P))
colnames(FA_PRE_cor.p) <- c("FA", "FA", "correlation coefficient", "p-value")
colnames(FA_POST_cor.p) <- c("FA", "FA", "correlation coefficient", "p-value")
Correlogram erstellen
corrplot(res.PRE, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
corrplot(res.POST, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
corrplot(res2.PRE$r, type="upper", order="hclust",
p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
corrplot(res2.PRE$r, type="upper", order="hclust",
p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
corrplot(res2.POST$r, type="upper", order="hclust",
p.mat = res2.POST$P, sig.level = 0.05, insig = "blank")
corrplot(res2.POST$r, type="upper", order="hclust", p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
Scatter Plot erstellen und Daten sichern
chart.Correlation(FA_stool_matrix_PRE, histogram=TRUE, pch=19)
chart.Correlation(FA_stool_matrix_POST, histogram = T, pch = 19)
write.table(FA_POST_cor.p, file ='/Users/student05/Documents/fa feces/FA fecal/correlations/FA post correlations cor p',sep ="\t", col.names = TRUE, row.names = FALSE)
write.table(FA_PRE_cor.p, file ='/Users/student05/Documents/fa feces/FA fecal/correlations/FA pre correlations cor p',sep ="\t", col.names = TRUE, row.names = FALSE)
2.3 Sterolkonvertierungstypen-Analyse der FA
Laden und filtern der Metadaten
FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
head=T)
FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))
FA_stool <- FA_stool[-c(65, 66), ]
row.names(FA_stool) <- FA_stool$SampleID
FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))
FA_stool[1,4]<- "PRE"
FA_stool_pairs <- filter(FA_stool, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
FA_stool_pairs_PP <- filter(FA_stool_pairs, Time=="PRE" | Time=="POST")
FA_stool_pairs_PPFU <- filter(FA_stool, Proband == "05AP" | Proband == "13
BS" | Proband == "17SK" | Proband == "22WS" | Proband ==
"40WA" | Proband == "41ML" | Proband == "54SL")
In high und low Converter unterteilen
lowconv <- filter(FA_stool, Proband == "05AP" | Proband == "33MP"
| Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
| Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")
lowconv['Phenotype'] = 'low converter'
highconv <- filter(FA_stool, Proband == "06WT" | Proband == "07RW"
| Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
| Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
| Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
| Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
highconv['Phenotype'] = 'high converter'
highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL
noconv <- filter(FA_stool, Proband == "28HM" | Proband == "32FG"
| Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
| Proband == "39DA" | Proband == "66DG" | Proband == "70PL")
noconv['Phenotype'] = 'not classified'
noconv$Converter.Type <- NULL
convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)
convT_paired <- filter(convT, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
convT_paired_PP <- filter(convT_paired, Time=="PRE" | Time=="POST")
convT_paired_PPnc <- filter(subset(convT_paired_PP, !Phenotype == "not classified" ))
convT_paired_PPnc.PRE <- filter(subset(convT_paired_PPnc, Time =="PRE"))
convT_paired_PPnc.POST <- filter(subset(convT_paired_PPnc, Time =="POST"))
write.table(convT, file = '/Users/student05/Documents/fa feces/FA sterol converter types ', sep = "\t", col.names = TRUE,row.names = FALSE)
In high und low Converter unterteilen
lowconv <- filter(FA_stool, Proband == "05AP" | Proband == "33MP"
| Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
| Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")
lowconv['Phenotype'] = 'low converter'
highconv <- filter(FA_stool, Proband == "06WT" | Proband == "07RW"
| Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
| Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
| Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
| Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
highconv['Phenotype'] = 'high converter'
highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL
noconv <- filter(FA_stool, Proband == "28HM" | Proband == "32FG"
| Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
| Proband == "39DA" | Proband == "66DG" | Proband == "70PL")
noconv['Phenotype'] = 'not classified'
noconv$Converter.Type <- NULL
convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)
convT_paired <- filter(convT, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
convT_paired_PP <- filter(convT_paired, Time=="PRE" | Time=="POST")
convT_paired_PPnc <- filter(subset(convT_paired_PP, !Phenotype == "not classified" ))
convT_paired_PPnc.PRE <- filter(subset(convT_paired_PPnc, Time =="PRE"))
convT_paired_PPnc.POST <- filter(subset(convT_paired_PPnc, Time =="POST"))
write.table(convT, file = '/Users/student05/Documents/fa feces/FA sterol converter types ', sep = "\t", col.names = TRUE,row.names = FALSE)
Boxplot aller FA je nach Sterolkonvertierungstyp und Zeitpunkt Melt Datenset Alle FA
FA_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype', 'Time'), measure.vars = c('sat', 'mono.unsat', 'di.unsat', 'more.2.unsat', 'less.14', 'c14.17', 'c18.19', 'c20.21', 'c22.24', 'total'))
FA_stool.melt <- subset(filter(FA_stool.melt, !Phenotype == "not classified"))
FA_stool.melt <- rename(FA_stool.melt, FA=variable)
FA_stool.melt <- rename(FA_stool.melt, Concentration=value)
ggplot(FA_stool.melt,aes(x=Phenotype, y=Concentration, fill= FA)) +
xlab ('Converter type') + ylab ('Concentration [nmol/g DW]') +
geom_boxplot()+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
facet_grid(.~Time)+
scale_fill_manual(labels = c("saturated", "monounsaturated", "diunsaturated", "> 2 unsaturated", "< c14", "c 14-17", "c 18-19", "c 20-21", "c 22-24", "total"),
values = c("tomato", "yellowgreen", "steelblue2", "orchid2", "deeppink", "brown4", "darkorange1", "blueviolet", "aquamarine3", "darksalmon"))
+theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Gesaettigte FA + wilcoxon test in Arbeit
sat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('sat'))
sat_stool.melt <- rename(sat_stool.melt, FA=variable)
sat_stool.melt <- rename(sat_stool.melt, Concentration=value)
ggplot(filter(sat_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/g]') +
scale_fill_manual(labels=c("Saturated fatty acids"), values = c("yellowgreen"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(filter(sat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') +
scale_fill_manual(labels=c("saturated fatty acid"), values = c("yellowgreen"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
mean(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "high converter"))$sat)
sd(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "high converter"))$sat)
mean(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "high converter"))$sat)
sd(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "high converter"))$sat)
mean(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "low converter"))$sat)
sd(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "low converter"))$sat)
mean(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "low converter"))$sat)
sd(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "low converter"))$sat)
pairwise.wilcox.test(subset(filter(convT_paired_PP, Time == "PRE"))$sat, subset(filter(convT_paired_PP, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired_PP, Time == "POST"))$sat, subset(filter(convT_paired_PP, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired_PP, Phenotype == "low converter"))$sat, subset(filter(convT_paired_PP, Phenotype == "low converter"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired_PP, Phenotype == "high converter"))$sat, subset(filter(convT_paired_PP, Phenotype == "high converter"))$Time, p.adjust.method = 'BH', paired = F)
sat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('sat'))
sat_stool.melt$Time <-factor(sat_stool.melt$Time, levels = c("PRE", "POST"))
sat_stool.melt <- dplyr::rename(sat_stool.melt, FA=variable)
sat_stool.melt <- dplyr::rename(sat_stool.melt, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/converter.sat.pdf",width=8, height=10)
ggplot(filter(sat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= Phenotype)) +facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Gesättigte Fettsäurenkonzentration [nmol/g] ') +
scale_fill_manual(labels=c("high converter", "low converter"), values = c("seashell4", "seashell2"))+
geom_boxplot(width = .7, lwd=0.6) + theme_classic() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("low converter")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
dev.off()
Total FA
total_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('total'))
total_stool.melt <- rename(total_stool.melt, FA=variable)
total_stool.melt <- rename(total_stool.melt, Concentration=value)
ggplot(filter(total_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/g]') +
scale_fill_manual(labels=c("total fatty acids"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
ggplot(filter(total_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') +
scale_fill_manual(labels=c("total fatty acid"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
einfach ungesaettigt
mono.unsat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('mono.unsat'))
mono.unsat_stool.melt <- rename(mono.unsat_stool.melt, FA=variable)
mono.unsat_stool.melt <- rename(mono.unsat_stool.melt, Concentration=value)
ggplot(filter(mono.unsat_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/g]') +
scale_fill_manual(labels=c("mono unsaturated fatt acids"), values = c("coral2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
ggplot(filter(mono.unsat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') +
scale_fill_manual(labels=c("mono unsaturated fatty acid"), values = c("coral2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))
zweifach ungesaettigt + wilcoxon test in Arbeit
di.unsat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('di.unsat'))
di.unsat_stool.melt <- rename(di.unsat_stool.melt, FA=variable)
di.unsat_stool.melt <- rename(di.unsat_stool.melt, Concentration=value)
ggplot(filter(di.unsat_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/g]') +
scale_fill_manual(labels=c("diunsaturated fatty acid"), values = c("seashell4"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
mean(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "high converter"))$di.unsat)
sd(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "high converter"))$di.unsat)
mean(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "high converter"))$di.unsat)
sd(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "high converter"))$di.unsat)
mean(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "low converter"))$di.unsat)
sd(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "low converter"))$di.unsat)
mean(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "low converter"))$di.unsat)
sd(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "low converter"))$di.unsat)
pairwise.wilcox.test(subset(filter(convT_paired_PP, Time == "PRE"))$di.unsat, subset(filter(convT_paired_PP, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired_PP, Time == "POST"))$di.unsat, subset(filter(convT_paired_PP, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired_PP, Phenotype == "low converter"))$di.unsat, subset(filter(convT_paired_PP, Phenotype == "low converter"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired_PP, Phenotype == "high converter"))$di.unsat, subset(filter(convT_paired_PP, Phenotype == "high converter"))$Time, p.adjust.method = 'BH', paired = F)
pdf("/Users/student05/Documents/fertige Plots/converter.unsat.pdf",width=8, height=10)
ggplot(filter(di.unsat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= Phenotype)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Zweifach ungesättigte Fettsäurenkonzentration [nmol/g] ') +
scale_fill_manual(labels=c("high converter", "low converter"), values = c("seashell4", "seashell2"))+
geom_boxplot(width = .7, lwd=0.6) + theme_classic() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
dev.off()
Omega6/Omega3-ratio
sat_stool.omega <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('ratio'))
sat_stool.omega$Time <-factor(sat_stool.omega$Time, levels = c("PRE", "POST"))
sat_stool.omega <- dplyr::rename(sat_stool.omega, FA=variable)
sat_stool.omega <- dplyr::rename(sat_stool.omega, Concentration=value)
ggplot(filter(sat_stool.omega, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= Phenotype)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Gesättigte Fettsäurenkonzentration [nmol/g] ') +
scale_fill_manual(labels=c("high converter", "low converter"), values = c("seashell4", "seashell2"))+
geom_boxplot(width = .7, lwd=0.6) + theme_classic() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("low converter", "high converter")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
mehr als zweifach ungesaettigt
more.2.unsat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('more.2.unsat'))
more.2.unsat_stool.melt <- rename(more.2.unsat_stool.melt, FA=variable)
more.2.unsat_stool.melt <- rename(more.2.unsat_stool.melt, Concentration=value)
ggplot(filter(more.2.unsat_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/g]') +
scale_fill_manual(labels=c("> 2 unsaturated"), values = c("brown4"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
ggplot(filter(more.2.unsat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') +
scale_fill_manual(labels=c("> 2 unsaturated"), values = c("brown4"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))
C18
c18.19_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('c18.19'))
c18.19_stool.melt <- rename(c18.19_stool.melt, FA=variable)
c18.19_stool.melt <- rename(c18.19_stool.melt, Concentration=value)
ggplot(filter(c18.19_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/g]') +
scale_fill_manual(labels=c("18-19 c-atomes"), values = c("yellow2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
ggplot(filter(c18.19_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') +
scale_fill_manual(labels=c("18-19 c-atomes"), values = c("yellow2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))
2.3 Alpha-Diversitaet und FAs Shannon und Simpson
Laden und filtern der Metadaten
map_alphadiv <- read.table("/Users/student05/Downloads/means_alpha_div.txt", sep = '\t', comment='',head = TRUE, row.names = 1)
FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
head=T)
View(FA_stool)
FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))
FA_stool <- FA_stool[-c(58:64), ]
row.names(FA_stool) <- FA_stool$SampleID
Synchonisieren der Datensets
common.ids.St <- intersect(rownames(FA_stool), rownames(map_alphadiv))
common.ids.St <- intersect(row.names(FA_stool), row.names(map_alphadiv))
FA_stool <- FA_stool[common.ids.St,]
map_alphadiv <- map_alphadiv[common.ids.St,]
FA_stool$Shannon <- map_alphadiv$Shannon
FA_stool$Simpson <- map_alphadiv$Simpson
Loop Korrelation Shannon und FAs
corr_colnames_FA <-colnames(FA_stool[,7:18])
corr_spearman_Shannon_FA <- data.frame()
for( i in unique(corr_colnames_FA)) {
tmp <- filter(FA_stool, !is.na(i))
x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y = t(as.matrix(tmp$Shannon) )
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
w = t(as.matrix(subset(filter(tmp, Time == "PRE"))$Shannon))
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Shannon))
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Shannon_FA)+1
corr_spearman_Shannon_FA[nrow,"Div"] = "Shannon"
corr_spearman_Shannon_FA[nrow, "column"] = i
corr_spearman_Shannon_FA[nrow, "rho"] = rho
corr_spearman_Shannon_FA[nrow, "p.value"] = p
corr_spearman_Shannon_FA[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Shannon_FA[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Shannon_FA[nrow, "rho_POST"] = rho_POST
corr_spearman_Shannon_FA[nrow, "p.value_POST"] = p_POST
}
corr_spearman_Shannon_FA$p.adjusted <- p.adjust(corr_spearman_Shannon_FA$p.value,method = "BH", n = 12)
corr_spearman_Shannon_FA$p.adjusted_PRE <-p.adjust(corr_spearman_Shannon_FA$p.value_PRE, method = "BH", n = 12)
corr_spearman_Shannon_FA$p.adjusted_POST <- p.adjust(corr_spearman_Shannon_FA$p.value_POST, method = "BH", n = 12)
write.table(corr_spearman_Shannon_FA, file = '/Users/student05/Documents/fa feces/tabellen/Shannon.FA.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Shannon und Fas die interessante Korrelation zeigen
ggplot(FA_stool, aes(x=total, y=Shannon)) + geom_point(aes(color=Time)) +
scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Total fatty acid Concentration [nmol/g]') +
ylab('Shannon-Index')
ggscatter(FA_stool, x='total', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time, scales = "free_x")+
theme(legend.position="none")
ggscatter(FA_stool, x='sat', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time)
ggscatter(FA_stool, x='anteiso', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Anteiso fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time)
ggscatter(FA_stool, x='c22.24', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 22-24 fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time)
ggscatter(FA_stool, x='c20.21', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-21 fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(FA_stool, x='c14.17', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time)
ggscatter(FA_stool, x='less.14', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '< 14 c-atoms fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time)
ggscatter(FA_stool, x='more.2.unsat', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time)
ggscatter(FA_stool, x='di.unsat', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Diunsaturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
facet_wrap(~Time)
ggscatter(FA_stool, x='sat', y='Shannon', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')
ggscatter(FA_stool, x='di.unsat', y='Shannon', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Diunsaturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')
ggscatter(FA_stool, x='more.2.unsat', y='Shannon', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')
ggscatter(FA_stool, x='c20.21', y='Shannon', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c20-21 c atoms fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')
ggscatter(FA_stool, x='c22.24', y='Shannon', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c22-24 c atoms fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')
in der Arbeit Total FA
pdf("/Users/student05/Documents/fertige Plots/Shannon.totalFA.pdf",width=8, height=10)
ggscatter(FA_stool, x='total', y='Shannon', add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0,7), cor.coef.size = 8, xlab= 'Gesamte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Shannon-Index')+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text( hjust=1))+
geom_point(color='black', size=2.5)
dev.off()
Korrelation zwischen Shannon und Kettenlaenge Laden der Metadaten
FA_stool.sh <- read.table("/Users/student05/Documents/DB shannon.txt", sep = '\t', comment='',
head=T)
FA_stool_sh_pr <- subset(filter(FA_stool.sh, !Time =="POST"))
FA_stool_sh_po <- subset(filter(FA_stool.sh, !Time =="PRE"))
Plotten der Daten
FA_stool_sh_pr$Shannon <- as.discrete(FA_stool_sh_pr$Shannon)
FA_stool_sh_pr$Concentration <- as.discrete(FA_stool_sh_pr$Concentration)
ggscatter(FA_stool_sh_pr, x='Concentration', y='Shannon',color = 'DB', palette = c('tomato', 'yellowgreen','steelblue'), add = 'reg.line', conf.int = T,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Concentration [nmol/g]', ylab = 'Shannon Index')+
facet_grid(.~ DB, scales="free")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(FA_stool_sh_po, x='Concentration', y='Shannon',color = 'DB', add = 'reg.line', conf.int = T,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(250, 7),xlab= 'Concentration [nmol/g]', ylab = 'Shannon Index')+
facet_grid(.~ DB, scales="free")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop Simpson und alle FAs
corr_spearman_Simpson_FA <- data.frame()
for( i in unique(corr_colnames_FA)) {
tmp <- filter(FA_stool, !is.na(i))
x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y = t(as.matrix(tmp$Simpson))
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
w = t(as.matrix (subset(filter(tmp, Time == "PRE"))$Simpson))
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Simpson))
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Simpson_FA)+1
corr_spearman_Simpson_FA[nrow,"Div"] = "Simpson"
corr_spearman_Simpson_FA[nrow, "column"] = i
corr_spearman_Simpson_FA[nrow, "rho"] = rho
corr_spearman_Simpson_FA[nrow, "p.value"] = p
corr_spearman_Simpson_FA[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Simpson_FA[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Simpson_FA[nrow, "rho_POST"] = rho_POST
corr_spearman_Simpson_FA[nrow, "p.value_POST"] = p_POST
}
corr_spearman_Simpson_FA$p.adjusted <- p.adjust(corr_spearman_Simpson_FA$p.value,method = "BH", n = 12)
corr_spearman_Simpson_FA$p.adjusted_PRE <-p.adjust(corr_spearman_Simpson_FA$p.value_PRE, method = "BH", n = 12)
corr_spearman_Simpson_FA$p.adjusted_POST <- p.adjust(corr_spearman_Simpson_FA$p.value_POST, method = "BH", n = 12)
write.table(corr_spearman_Simpson_FA, file = '/Users/student05/Documents/fa feces/tabellen/Simpson.FA.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
corr_sig_Simpson_FA <- filter(corr_spearman_Simpson_FA, p.adjusted < 0.05 | p.adjusted_PRE < 0.5 | p.adjusted_POST < 0.5 | p.adjusted_FU < 0.5)
Plotten der Metadaten Simpson und interessante FAs
ggscatter(FA_stool, x='total', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'),
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Saturated fatty acids Concentration [nmol/g]', ylab = 'Simpson-Index') +
facet_wrap(~Time, scales = "free_x")
ggscatter(FA_stool, x='total', y='Simpson', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'),
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Total fatty acids Concentration [nmol/g]', ylab = 'Simpson-Index') +
facet_wrap(~Time)
ggscatter(FA_stool, x='sat', y='Simpson',
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Saturated fatty acids Concentration [nmol/g]', ylab = 'Simpson-Index')
ggscatter(FA_stool, x='total', y='Simpson',
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Total fatty acids Concentration [nmol/g]', ylab = 'Simpson-Index')
2.4 Korrelationsanalysen zwischen Taxa und den FAs
Laden und filtern der Metadaten, Pylum und Genus level setzen, Daten sichern
relab_means <- read.table('/Users/student05/Documents/relative abundance/relab_means_per_timepoint.txt', sep ='\t', comment='', head=T)
relab_means_melt <- melt(relab_means, id=c('Proband', 'Time'))
relab_means_melt <- dplyr::rename(relab_means_melt, Taxa=variable)
relab_means_melt <- dplyr::rename(relab_means_melt, Relative_Abundance=value)
relab_phylum <- subset(relab_means_melt, !grepl("g__|f__|o__|c__", relab_means_melt$Taxa))
relab_phylum <- subset(relab_phylum, !grepl("k__Archaea", relab_phylum$Taxa))
relab_phylum$Time <- factor(relab_phylum$Time, levels=c('PRE','POST','FOLLOW-UP'))
relab_phylum_spread <- spread(relab_phylum, Taxa, Relative_Abundance, sep = NULL)
relab_genus <- subset(relab_means_melt, grepl("g__", relab_means_melt$Taxa))
relab_genus <- subset(relab_genus, !grepl("k__Archaea", relab_genus$Taxa))
relab_genus$Time <- factor(relab_genus$Time, levels = c('PRE','POST','FOLLOW-UP'))
relab_genus_spread <- spread(relab_genus, Taxa, Relative_Abundance, sep = NULL)
write.table(relab_phylum_spread, file = '/Users/student05/Documents/relative abundance/relab_phylum.txt', sep= "\t", col.names = TRUE, row.names = FALSE)
write.table(relab_genus_spread, file = '/Users/student05/Documents/relative abundance/relab_genus.txt', sep ="\t", col.names = TRUE, row.names = FALSE)
relab_phylum_spread <- read.table("/Users/student05/Documents/relative abundance/relab_phylum.txt", sep = '\t', comment='',
head=T)
relab_genus_spread <- read.table("/Users/student05/Documents/relative abundance/relab_genus.txt", sep = '\t', comment='',
head=T)
Synchonisieren der Metadatensets und Laden der FA-Metadaten
relab_phylum_ID <- relab_phylum_spread
relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))
row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID
relab_genus_ID <- relab_genus_spread
relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))
row.names(relab_genus_ID) <- relab_genus_ID$SampleID
FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
head=T)
FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))
FA_stool <- subset(filter(FA_stool, !Time == "FOLLOW-UP"))
FA_stool <- subset(filter(FA_stool, !Proband == "31KE", !Proband == "34WF",
!Proband == "45GL", !Proband == "49RJ", !Proband == "54SL", !Proband == "74SA"))
FA_stool <- mutate(FA_stool, SampleID1 = paste(Proband, Time, sep = "."))
row.names(FA_stool) <- FA_stool$SampleID
common.ids.relab <- intersect(rownames(FA_stool), rownames(relab_phylum_ID))
FA_stool <- FA_stool[common.ids.relab,]
relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
write.table(FA_stool, file = '/Users/student05/Documents/fa feces/FA fecal/relative abundance/FA_stool_total.txt', sep= "\t", col.names = TRUE, row.names = FALSE)
Erstellen des Phylum-Datensets und hinzufuegen des Log und Pseudocounts 0.00001
phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])
relab_phylum_ID <- relab_phylum_ID[,c(3:8)]+ 0.00001
relab_phylum_ID_log <- log10(relab_phylum_ID_log)
phylum_FA <- cbind(relab_phylum_ID, FA_stool[, c(1:19)])
phylum_FA$Time <- factor(phylum_FA$Time, levels = c("PRE", "POST"))
Loop fuer Korrelation zwischen gesaettigten FA und Phylum-Level
corr_map_phylum_sat <- filter(phylum_FA, !is.na(sat))
corr_spearman_Phylum_sat <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_sat, !is.na(i))
y = tmp[,i]
x = tmp$sat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$sat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$sat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_sat)+1
corr_spearman_Phylum_sat[nrow,"FA"] <- "sat"
corr_spearman_Phylum_sat[nrow, "Phylum"] = i
corr_spearman_Phylum_sat[nrow, "p.value"] = p
corr_spearman_Phylum_sat[nrow, "rho"] = rho
corr_spearman_Phylum_sat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_sat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_sat[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_sat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_sat$p.adjusted <- p.adjust(corr_spearman_Phylum_sat$p.value, method = "BH", n = 35)
corr_spearman_Phylum_sat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_sat$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_sat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_sat$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_sat <- filter(corr_spearman_Phylum_sat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_sat, file = '/Users/student05/Documents/fa feces/tabellen/sat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten der gesaettigten FA und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time, scales="free_x")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Proteobacteria')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Proteobacteria', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -1.7), cor.coef.size = 6, xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Proteobacteria')+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=0, hjust=1))
ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(250, -1), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Firmicutes', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(250, -0.75), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Firmicutes')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
stat_cor(method = "pearson", label.x = 3, label.y = 30)
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)
in Arbeit
pdf("/Users/student05/Documents/fertige Plots/sat.verru.pdf",width=8, height=10)
ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia', palette = c('tomato', 'yellowgreen'), add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE,
cor.coef = TRUE, cor.method = '',cor.coef.size = 8,xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen p__Verrucomicrobia [%]')+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
geom_point(color='black', size=2.5)+
scale_y_log10(labels = percent_format())
dev.off()
pdf("/Users/student05/Documents/fertige Plots/sat.bacteroidetes.pdf",width=8, height=10)
ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', size = 2.5,conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(0, -0.8),cor.coef.size = 7, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen p__Bacteroidetes [%]')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
scale_y_log10(labels = percent_format())
dev.off()
pdf("/Users/student05/Documents/fertige Plots/unsat.bacteroidetes.pdf",width=8, height=10)
ggscatter(phylum_FA, x='unsat', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', size = 2.5,conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(0, -0.8),cor.coef.size = 7, xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen p__Bacteroidetes [%]')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
scale_y_log10(labels = percent_format())
dev.off()
pdf("/Users/student05/Documents/fertige Plots/omega3.bacteroidetes.neu.pdf",width=8, height=10)
ggscatter(phylum_FA, x='Omega3', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', size = 2.5,conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(0, -0.8),cor.coef.size = 7, xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen p__Bacteroidetes [%]')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
scale_y_log10(labels = percent_format())
dev.off()
pdf("/Users/student05/Documents/fertige Plots/sat.akkermansia.pdf",width=8, height=10)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', palette = c('tomato', 'yellowgreen'), add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8),cor.coef.size = 8, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'log10 (Relatives Vorkommen g__Akkermansia)')+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")+
geom_point(color='grey52')
dev.off()
Loop einfach ungesaettigte FA und Phylum-Level
corr_map_phylum_mono.unsat <- filter(phylum_FA, !is.na(mono.unsat))
corr_spearman_Phylum_mono.unsat <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_mono.unsat, !is.na(i))
y = tmp[,i]
x = tmp$mono.unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$mono.unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$mono.unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_mono.unsat)+1
corr_spearman_Phylum_mono.unsat[nrow,"FA"] <- "mono.unsat"
corr_spearman_Phylum_mono.unsat[nrow, "Phylum"] = i
corr_spearman_Phylum_mono.unsat[nrow, "p.value"] = p
corr_spearman_Phylum_mono.unsat[nrow, "rho"] = rho
corr_spearman_Phylum_mono.unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_mono.unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_mono.unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_mono.unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_mono.unsat$p.adjusted <- p.adjust(corr_spearman_Phylum_mono.unsat$p.value, method = "BH", n = 35)
corr_spearman_Phylum_mono.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_mono.unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_mono.unsat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_mono.unsat$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_mono.unsat <- filter(corr_spearman_Phylum_sat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_mono.unsat, file = '/Users/student05/Documents/fa feces/tabellen/mono.unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von einfach ungesaettigten FA und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Monounsaturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='mono.unsat', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Monounsaturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Proteobacteria')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Monounsaturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='mono.unsat', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Monounsaturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Actinobacteria')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop zweifach ungesaettigten FA und phylum-level
corr_map_phylum_more.2.unsat <- filter(phylum_FA, !is.na(more.2.unsat))
corr_spearman_Phylum_more.2.unsat <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_more.2.unsat, !is.na(i))
y = tmp[,i]
x = tmp$more.2.unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$more.2.unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$more.2.unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_more.2.unsat)+1
corr_spearman_Phylum_more.2.unsat[nrow,"FA"] <- "> 2 unsat"
corr_spearman_Phylum_more.2.unsat[nrow, "Phylum"] = i
corr_spearman_Phylum_more.2.unsat[nrow, "p.value"] = p
corr_spearman_Phylum_more.2.unsat[nrow, "rho"] = rho
corr_spearman_Phylum_more.2.unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_more.2.unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_more.2.unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_more.2.unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_more.2.unsat$p.adjusted <- p.adjust(corr_spearman_Phylum_more.2.unsat$p.value, method = "BH", n = 35)
corr_spearman_Phylum_more.2.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_more.2.unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_more.2.unsat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_more.2.unsat$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_more.2.unsat <- filter(corr_spearman_Phylum_more.2.unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_more.2.unsat, file = '/Users/student05/Documents/fa feces/tabellen/more.2.unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von zweifach ungesaettigten FA und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Diunsaturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Diunsaturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
geom_text(aes(label=Proband),hjust=0, vjust=0)
ggscatter(phylum_FA, x='more.2.unsat', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='more.2.unsat', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Diunsaturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop weniger 14C und phylum-level
corr_map_phylum_less.14 <- filter(phylum_FA, !is.na(less.14))
corr_spearman_Phylum_less.14 <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_less.14, !is.na(i))
y = tmp[,i]
x = tmp$less.14
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$less.14
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$less.14
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_less.14)+1
corr_spearman_Phylum_less.14[nrow,"FA"] <- "< 14 c"
corr_spearman_Phylum_less.14[nrow, "Phylum"] = i
corr_spearman_Phylum_less.14[nrow, "p.value"] = p
corr_spearman_Phylum_less.14[nrow, "rho"] = rho
corr_spearman_Phylum_less.14[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_less.14[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_less.14[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_less.14[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_less.14$p.adjusted <- p.adjust(corr_spearman_Phylum_less.14$p.value, method = "BH", n = 35)
corr_spearman_Phylum_less.14$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_less.14$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_less.14$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_less.14$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_less.14 <- filter(corr_spearman_Phylum_less.14, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_less.14, file = '/Users/student05/Documents/fa feces/tabellen/less.14.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten FA mit weniger als 14C atome und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('< 14 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
geom_text(aes(label=Proband),hjust=0, vjust=0)
ggscatter(phylum_FA, x='less.14', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Less 14c fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Proteobacteria')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop C14-17 FA und phylum-level
corr_map_phylum_c14.17 <- filter(phylum_FA, !is.na(c14.17 ))
corr_spearman_Phylum_c14.17 <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_c14.17 , !is.na(i))
y = tmp[,i]
x = tmp$c14.17
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$c14.17
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$c14.17
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_c14.17 )+1
corr_spearman_Phylum_c14.17 [nrow,"FA"] <- "c 14-17"
corr_spearman_Phylum_c14.17 [nrow, "Phylum"] = i
corr_spearman_Phylum_c14.17 [nrow, "p.value"] = p
corr_spearman_Phylum_c14.17 [nrow, "rho"] = rho
corr_spearman_Phylum_c14.17 [nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_c14.17 [nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_c14.17 [nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_c14.17 [nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_c14.17 $p.adjusted <- p.adjust(corr_spearman_Phylum_c14.17 $p.value, method = "BH", n = 35)
corr_spearman_Phylum_c14.17 $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_c14.17 $p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_c14.17 $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_c14.17 $p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_c14.17 <- filter(corr_spearman_Phylum_c14.17, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_c14.17, file = '/Users/student05/Documents/fa feces/tabellen/c14.17.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten C14-17 FA und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('14-17 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='c14.17', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Proteobacteria')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('< 14-17 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('< 14-17 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('< 14-17 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='c14.17', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(150, -0.7), xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Firmicutes')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='c14.17', y='k__Bacteria.p__Firmicutes', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(150, -0.7), xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Firmicutes')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
Loop C18 FA und phylum-level
orr_map_phylum_c18.19 <- filter(phylum_FA, !is.na(c18 ))
corr_spearman_Phylum_c18.19 <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_c18.19 , !is.na(i))
y = tmp[,i]
x = tmp$c18
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$c18
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$c18
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_c18.19 )+1
corr_spearman_Phylum_c18.19 [nrow,"FA"] <- "c 18-19"
corr_spearman_Phylum_c18.19 [nrow, "Phylum"] = i
corr_spearman_Phylum_c18.19 [nrow, "p.value"] = p
corr_spearman_Phylum_c18.19 [nrow, "rho"] = rho
corr_spearman_Phylum_c18.19 [nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_c18.19 [nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_c18.19 [nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_c18.19 [nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_c18.19 $p.adjusted <- p.adjust(corr_spearman_Phylum_c18.19 $p.value, method = "BH", n = 35)
corr_spearman_Phylum_c18.19 $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_c18.19 $p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_c18.19 $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_c18.19 $p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_c18.19 <- filter(corr_spearman_Phylum_c18.19, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_c18.19, file = '/Users/student05/Documents/fa feces/tabellen/c18.19.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von C18 FA und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('18-19 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('< 18-19 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('< 14-17 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='c18.19', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 18-19 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Verrucomicrobia')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop C20-24 und phylum-level
corr_map_phylum_c20.24 <- filter(phylum_FA, !is.na(c20.24 ))
corr_spearman_Phylum_c20.24 <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_c20.24 , !is.na(i))
y = tmp[,i]
x = tmp$c20.24
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$c20.24
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$c20.24
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_c20.24 )+1
corr_spearman_Phylum_c20.24 [nrow,"FA"] <- "c 20-24"
corr_spearman_Phylum_c20.24 [nrow, "Phylum"] = i
corr_spearman_Phylum_c20.24 [nrow, "p.value"] = p
corr_spearman_Phylum_c20.24 [nrow, "rho"] = rho
corr_spearman_Phylum_c20.24 [nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_c20.24 [nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_c20.24 [nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_c20.24 [nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_c20.24$p.adjusted <- p.adjust(corr_spearman_Phylum_c20.24$p.value, method = "BH", n = 35)
corr_spearman_Phylum_c20.24$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_c20.24$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_c20.24$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_c20.24$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_c20.24 <- filter(corr_spearman_Phylum_c20.24, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_c20.24, file = '/Users/student05/Documents/fa feces/tabellen/c20.24.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von C20-24 und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-21 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('< 18-19 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-21 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='c20.21', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-21 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-21 c-atoms fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='c20.21', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-21 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Tenericutes')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop total FA und phylum-level
corr_map_phylum_total <- filter(phylum_FA, !is.na(total ))
corr_spearman_Phylum_total <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_total , !is.na(i))
y = tmp[,i]
x = tmp$total
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$total
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$total
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_total )+1
corr_spearman_Phylum_total [nrow,"FA"] <- "total"
corr_spearman_Phylum_total [nrow, "Phylum"] = i
corr_spearman_Phylum_total [nrow, "p.value"] = p
corr_spearman_Phylum_total [nrow, "rho"] = rho
corr_spearman_Phylum_total [nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_total [nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_total [nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_total [nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_total $p.adjusted <- p.adjust(corr_spearman_Phylum_total $p.value, method = "BH", n = 35)
corr_spearman_Phylum_total $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_total $p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_total $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_total $p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_total <- filter(corr_spearman_Phylum_total, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_total, file = '/Users/student05/Documents/fa feces/tabellen/total.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von total FA und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='total', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Proteobacteria')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='total', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Actinobacteria')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(phylum_FA, x='c20.21', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-21 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Tenericutes')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop Omega3 und phylum-level
corr_map_phylum_Omega3 <- filter(phylum_FA, !is.na(Omega3 ))
corr_spearman_Phylum_Omega3 <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Omega3 , !is.na(i))
y = tmp[,i]
x = tmp$Omega3
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Omega3
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Omega3
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Omega3 )+1
corr_spearman_Phylum_Omega3 [nrow,"FA"] <- "Omega3"
corr_spearman_Phylum_Omega3 [nrow, "Phylum"] = i
corr_spearman_Phylum_Omega3 [nrow, "p.value"] = p
corr_spearman_Phylum_Omega3 [nrow, "rho"] = rho
corr_spearman_Phylum_Omega3 [nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Omega3 [nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Omega3[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Omega3[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Omega3$p.adjusted <- p.adjust(corr_spearman_Phylum_Omega3$p.value, method = "BH", n = 35)
corr_spearman_Phylum_Omega3$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Omega3$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Omega3$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Omega3$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_Omega3<- filter(corr_spearman_Phylum_Omega3, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_Omega3, file = '/Users/student05/Documents/fa feces/tabellen/Omega3.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten Omega3 und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=Omega3)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=Omega3)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Omega3)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
pdf("/Users/student05/Documents/fertige Plots/Linolsäure.bacteroidetes.pdf",width=8, height=10)
ggscatter(phylum_FA, x='Omega3', y='k__Bacteria.p__Bacteroidetes',color = 'Time', label = 'Proband',palette = c('skyblue', 'orchid'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(0, -0.9), cor.coef.size = 7,xlab= 'Fäkale alpha-Linolensäure Konzentrationen [nmol/g]', ylab = 'log10 (Relatives Vorkommen p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
dev.off()
Loop Omega 6 und Phylum-level
corr_map_phylum_Omega6 <- filter(phylum_FA, !is.na(Omega6 ))
corr_spearman_Phylum_Omega6 <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Omega6 , !is.na(i))
y = tmp[,i]
x = tmp$Omega6
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Omega6
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Omega6
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Omega6 )+1
corr_spearman_Phylum_Omega6 [nrow,"FA"] <- "Omega6"
corr_spearman_Phylum_Omega6 [nrow, "Phylum"] = i
corr_spearman_Phylum_Omega6 [nrow, "p.value"] = p
corr_spearman_Phylum_Omega6 [nrow, "rho"] = rho
corr_spearman_Phylum_Omega6 [nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Omega6 [nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Omega6 [nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Omega6 [nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Omega6 $p.adjusted <- p.adjust(corr_spearman_Phylum_Omega6 $p.value, method = "BH", n = 35)
corr_spearman_Phylum_Omega6 $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Omega6 $p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Omega6 $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Omega6 $p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_Omega6 <- filter(corr_spearman_Phylum_Omega6, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_Omega6, file = '/Users/student05/Documents/fa feces/tabellen/Omega6.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten omega6 und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=Omega6)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=Omega6)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Omega6)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=Linolsaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Linolsaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop omega6/omega3-ratio und phylum-level
corr_map_phylum_ratio <- filter(phylum_FA, !is.na(ratio))
corr_spearman_Phylum_ratio <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_ratio , !is.na(i))
y = tmp[,i]
x = tmp$ratio
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$ratio
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$ratio
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_ratio )+1
corr_spearman_Phylum_ratio [nrow,"FA"] <- "ratio"
corr_spearman_Phylum_ratio [nrow, "Phylum"] = i
corr_spearman_Phylum_ratio [nrow, "p.value"] = p
corr_spearman_Phylum_ratio [nrow, "rho"] = rho
corr_spearman_Phylum_ratio [nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_ratio [nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_ratio [nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_ratio [nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_ratio $p.adjusted <- p.adjust(corr_spearman_Phylum_ratio $p.value, method = "BH", n = 35)
corr_spearman_Phylum_ratio $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_ratio $p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_ratio $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_ratio $p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_ratio <- filter(corr_spearman_Phylum_ratio, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_ratio, file = '/Users/student05/Documents/fa feces/tabellen/ratio.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von Omega6/omega3-ratio und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=ratio)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=ratio)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=ratio)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=ratio)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('EPA intake [g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=ratio)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=ratio)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('DHA intake [g]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop ungesaettigte FA und Phylum-level
corr_map_phylum_unsat <- filter(phylum_FA, !is.na(unsat))
corr_spearman_Phylum_unsat <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_unsat , !is.na(i))
y = tmp[,i]
x = tmp$unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_unsat )+1
corr_spearman_Phylum_unsat [nrow,"FA"] <- "unsat"
corr_spearman_Phylum_unsat [nrow, "Phylum"] = i
corr_spearman_Phylum_unsat [nrow, "p.value"] = p
corr_spearman_Phylum_unsat [nrow, "rho"] = rho
corr_spearman_Phylum_unsat [nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_unsat [nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_unsat [nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_unsat [nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_unsat $p.adjusted <- p.adjust(corr_spearman_Phylum_unsat $p.value, method = "BH", n = 35)
corr_spearman_Phylum_unsat $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_unsat $p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_unsat $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_unsat $p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_unsat <- filter(corr_spearman_Phylum_unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_unsat, file = '/Users/student05/Documents/fa feces/tabellen/unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Analysen zwischen FA und Genus-level Datensets filtern und hinzufuegen von log und pseudocount 0.00001
genus_colnames <- colnames(relab_genus_spread[, c(3:31)])
relab_genus_ID <- relab_genus_ID[,c(3:31)] + 0.00001
relab_genus_ID_log <- log10(relab_genus_ID_log)
genus_FA <- cbind(relab_genus_ID, FA_stool[, c(1:19)])
genus_FA$Time <- factor(genus_FA$Time, levels = c("PRE", "POST"))
Loop gesaettigte FA und genus-level
corr_map_genus_sat <- filter(genus_FA, !is.na(sat))
corr_spearman_genus_sat <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_sat, !is.na(i))
y = tmp[,i]
x = tmp$sat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$sat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$sat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_sat)+1
corr_spearman_genus_sat[nrow,"FA"] = "saturated"
corr_spearman_genus_sat[nrow, "Genus"] = i
corr_spearman_genus_sat[nrow, "p.value"] = p
corr_spearman_genus_sat[nrow, "rho"] = rho
corr_spearman_genus_sat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_sat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_sat[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_sat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_sat$p.adjusted <- p.adjust(corr_spearman_genus_sat$p.value, method = "BH", n = 35)
corr_spearman_genus_sat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_sat$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_sat$p.adjusted_POST <- p.adjust(corr_spearman_genus_sat$p.value_POST, method = "BH", n = 35)
corr_sig_genus_sat <- filter(corr_spearman_genus_sat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_sat, file = '/Users/student05/Documents/fa feces/tabellen/sat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
PLotten von gesaettigten FA und genus-level
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(400, -1.3),cor.coef.size = 5, xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated Concentration [nmol/mg]') +
ylab('log10 (Relative Abundance g__Collinsella)')+
facet_wrap(~Time)
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab(' Concentration [mg/ml]') + ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
facet_wrap(~Time)
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acids Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance f__Rikenellaceae')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Faecalibacterium )')+
facet_wrap(~Time)
ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=sat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatt acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Akkermansia )')+
facet_wrap(~Time)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(400, -0.5), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(400, -0.5), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(500, -1), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
in Arbeit
pdf("/Users/student05/Documents/fertige Plots/sat.faecali.pdf",width=8, height=10)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium', palette = c('tomato', 'yellowgreen'), add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -1.3),cor.coef.size = 8, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen g__Faecalibacterium [%]')+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")+
geom_point(color='black', size=2.5)+
scale_y_log10(labels = percent_format())
dev.off()
pdf("/Users/student05/Documents/fertige Plots/sat.oscillo.pdf",width=8, height=10)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', palette = c('tomato', 'yellowgreen'), add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -2),cor.coef.size = 8, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen g__Oscillospira [%]')+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")+
geom_point(color='black', size=2.5)+
scale_y_log10(labels = percent_format())
dev.off()
pdf("/Users/student05/Documents/fertige Plots/sat.akkermansia.pdf",width=8, height=10)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', palette = c('tomato', 'yellowgreen'), add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.7),cor.coef.size = 8, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab ='Relatives Vorkommen g__Akkermansia [%]')+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")+
geom_point(color='black', size=2.5)+
scale_y_log10(labels = percent_format())
dev.off()
Loop einfach ungesaettigte FA und genus-level
corr_map_genus_mono.unsat <- filter(genus_FA, !is.na(mono.unsat))
corr_spearman_genus_mono.unsat <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_mono.unsat, !is.na(i))
y = tmp[,i]
x = tmp$mono.unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$mono.unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$mono.unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_mono.unsat)+1
corr_spearman_genus_mono.unsat[nrow,"FA"] = "mono.unsaturated"
corr_spearman_genus_mono.unsat[nrow, "Genus"] = i
corr_spearman_genus_mono.unsat[nrow, "p.value"] = p
corr_spearman_genus_mono.unsat[nrow, "rho"] = rho
corr_spearman_genus_mono.unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_mono.unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_mono.unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_mono.unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_mono.unsat$p.adjusted <- p.adjust(corr_spearman_genus_mono.unsat$p.value, method = "BH", n = 35)
corr_spearman_genus_mono.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_mono.unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_mono.unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_mono.unsat$p.value_POST, method = "BH", n = 35)
corr_sig_genus_mono.unsat <- filter(corr_spearman_genus_mono.unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_mono.unsat, file = '/Users/student05/Documents/fa feces/tabellen/mono.unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von einfach ungesaettigten FA und genus-level
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time)
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bacteroides)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Sutterella)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Prevotella)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated Concentration [nmol/mg]') +
ylab('log10 (Relative Abundance g__Collinsella)')+
facet_wrap(~Time)
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab(' Concentration [mg/ml]') + ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
facet_wrap(~Time)
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acids Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Faecalibacterium )')+
facet_wrap(~Time)
ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=mono.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatt acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Akkermansia )')+
facet_wrap(~Time)
Loop zweifach ungesaettigte FA und genus-level
corr_map_genus_di.unsat <- filter(genus_FA, !is.na(di.unsat))
corr_spearman_genus_di.unsat <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_di.unsat, !is.na(i))
y = tmp[,i]
x = tmp$di.unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$di.unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$di.unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_di.unsat)+1
corr_spearman_genus_di.unsat[nrow,"FA"] = "di.unsaturated"
corr_spearman_genus_di.unsat[nrow, "Genus"] = i
corr_spearman_genus_di.unsat[nrow, "p.value"] = p
corr_spearman_genus_di.unsat[nrow, "rho"] = rho
corr_spearman_genus_di.unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_di.unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_di.unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_di.unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_di.unsat$p.adjusted <- p.adjust(corr_spearman_genus_di.unsat$p.value, method = "BH", n = 35)
corr_spearman_genus_di.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_di.unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_di.unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_di.unsat$p.value_POST, method = "BH", n = 35)
corr_sig_genus_di.unsat <- filter(corr_spearman_genus_di.unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_di.unsat, file = '/Users/student05/Documents/fa feces/tabellen/di.unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
PLotten von zweifach ungesaettigten FAs und genus-level
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=di.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=di.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bacteroides)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=di.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Sutterella)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, x=di.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Dorea)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister, x=di.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Dialister)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop mehr als zweifach ungesaettigte FAs und genus-level
corr_map_genus_more.2.unsat <- filter(genus_FA, !is.na(more.2.unsat))
corr_spearman_genus_more.2.unsat <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_more.2.unsat, !is.na(i))
y = tmp[,i]
x = tmp$more.2.unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$more.2.unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$more.2.unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_more.2.unsat)+1
corr_spearman_genus_more.2.unsat[nrow,"FA"] = "more.2.unsaturated"
corr_spearman_genus_more.2.unsat[nrow, "Genus"] = i
corr_spearman_genus_more.2.unsat[nrow, "p.value"] = p
corr_spearman_genus_more.2.unsat[nrow, "rho"] = rho
corr_spearman_genus_more.2.unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_more.2.unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_more.2.unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_more.2.unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_more.2.unsat$p.adjusted <- p.adjust(corr_spearman_genus_more.2.unsat$p.value, method = "BH", n = 35)
corr_spearman_genus_more.2.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_more.2.unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_more.2.unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_more.2.unsat$p.value_POST, method = "BH", n = 35)
corr_sig_genus_more.2.unsat <- filter(corr_spearman_genus_more.2.unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_more.2.unsat, file = '/Users/student05/Documents/fa feces/tabellen/more.2.unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von mehr als zweifach ungesaettigten FA und genus-level
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time) +
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bacteroides)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Sutterella)')+
facet_wrap(~Time)+
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))+
geom_text(aes(label=Proband),hjust=0, vjust=0)
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Collinsella)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister, x=more.2.unsat)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Dialister)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop FA weniger als 14C und genus-level
corr_map_genus_less.14 <- filter(genus_FA, !is.na(less.14))
corr_spearman_genus_less.14 <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_less.14, !is.na(i))
y = tmp[,i]
x = tmp$less.14
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$less.14
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$less.14
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_less.14)+1
corr_spearman_genus_less.14[nrow,"FA"] = "less.14"
corr_spearman_genus_less.14[nrow, "Genus"] = i
corr_spearman_genus_less.14[nrow, "p.value"] = p
corr_spearman_genus_less.14[nrow, "rho"] = rho
corr_spearman_genus_less.14[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_less.14[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_less.14[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_less.14[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_less.14$p.adjusted <- p.adjust(corr_spearman_genus_less.14$p.value, method = "BH", n = 35)
corr_spearman_genus_less.14$p.adjusted_PRE <- p.adjust(corr_spearman_genus_less.14$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_less.14$p.adjusted_POST <- p.adjust(corr_spearman_genus_less.14$p.value_POST, method = "BH", n = 35)
corr_sig_genus_less.14 <- filter(corr_spearman_genus_less.14, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_less.14, file = '/Users/student05/Documents/fa feces/tabellen/less.14.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten weniger als 14C FA und genus-level
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('less.14urated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bacteroides)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('less.14urated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Sutterella)')+
facet_wrap(~Time)+
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Collinsella)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium., x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Eubacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Prevotella)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Faecalibacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium., x=less.14)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Eubacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop C14-17 FA und genus-level
corr_map_genus_c14.17 <- filter(genus_FA, !is.na(c14.17))
corr_spearman_genus_c14.17 <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_c14.17, !is.na(i))
y = tmp[,i]
x = tmp$c14.17
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$c14.17
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$c14.17
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_c14.17)+1
corr_spearman_genus_c14.17[nrow,"FA"] = "c14.17"
corr_spearman_genus_c14.17[nrow, "Genus"] = i
corr_spearman_genus_c14.17[nrow, "p.value"] = p
corr_spearman_genus_c14.17[nrow, "rho"] = rho
corr_spearman_genus_c14.17[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_c14.17[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_c14.17[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_c14.17[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_c14.17$p.adjusted <- p.adjust(corr_spearman_genus_c14.17$p.value, method = "BH", n = 35)
corr_spearman_genus_c14.17$p.adjusted_PRE <- p.adjust(corr_spearman_genus_c14.17$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_c14.17$p.adjusted_POST <- p.adjust(corr_spearman_genus_c14.17$p.value_POST, method = "BH", n = 35)
corr_sig_genus_c14.17 <- filter(corr_spearman_genus_c14.17, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_c14.17, file = '/Users/student05/Documents/fa feces/tabellen/c14.17.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von C14-17 FA und genus-level
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 14-17 fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time) +
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 14-17 fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bacteroides)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c14.17urated fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Sutterella)')+
facet_wrap(~Time)+
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab(' 14-17 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Collinsella)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('14-17 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Akkermansia)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 14-17 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Faecalibacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('14-17 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
genus_FA$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__, x=c14.17)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('14-17 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Barnesiellaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='c14.17', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='c14.17', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Loop C18 FA und genus-level
corr_map_genus_c18 <- filter(genus_FA, !is.na(c18))
corr_spearman_genus_c18 <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_c18, !is.na(i))
y = tmp[,i]
x = tmp$c18
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$c18
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$c18
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_c18)+1
corr_spearman_genus_c18[nrow,"FA"] = "c18"
corr_spearman_genus_c18[nrow, "Genus"] = i
corr_spearman_genus_c18[nrow, "p.value"] = p
corr_spearman_genus_c18[nrow, "rho"] = rho
corr_spearman_genus_c18[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_c18[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_c18[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_c18[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_c18$p.adjusted <- p.adjust(corr_spearman_genus_c18$p.value, method = "BH", n = 35)
corr_spearman_genus_c18$p.adjusted_PRE <- p.adjust(corr_spearman_genus_c18$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_c18$p.adjusted_POST <- p.adjust(corr_spearman_genus_c18$p.value_POST, method = "BH", n = 35)
corr_sig_genus_c18 <- filter(corr_spearman_genus_c18, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_c18, file = '/Users/student05/Documents/fa feces/tabellen/c18.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten C18 FA und genus-level
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 18 fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time) +
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 18 fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bacteroides)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Sutterella)')+
facet_wrap(~Time)+
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Ruminococcaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Akkermansia)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 18 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Faecalibacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__, x=c18.19)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Barnesiellaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop C20-24 FA und genus-level
corr_map_genus_c20.24 <- filter(genus_FA, !is.na(c20.24))
corr_spearman_genus_c20.24 <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_c20.24, !is.na(i))
y = tmp[,i]
x = tmp$c20.24
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$c20.24
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$c20.24
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_c20.24)+1
corr_spearman_genus_c20.24[nrow,"FA"] = "c20.24"
corr_spearman_genus_c20.24[nrow, "Genus"] = i
corr_spearman_genus_c20.24[nrow, "p.value"] = p
corr_spearman_genus_c20.24[nrow, "rho"] = rho
corr_spearman_genus_c20.24[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_c20.24[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_c20.24[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_c20.24[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_c20.24$p.adjusted <- p.adjust(corr_spearman_genus_c20.24$p.value, method = "BH", n = 35)
corr_spearman_genus_c20.24$p.adjusted_PRE <- p.adjust(corr_spearman_genus_c20.24$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_c20.24$p.adjusted_POST <- p.adjust(corr_spearman_genus_c20.24$p.value_POST, method = "BH", n = 35)
corr_sig_genus_c20.24 <- filter(corr_spearman_genus_c20.24, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_c20.24, file = '/Users/student05/Documents/fa feces/tabellen/c20.24.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von C20-24 FA und genus-level
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 20-24 fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time) +
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='c20.21', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-24 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 20-24 fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bacteroides)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Sutterella)')+
facet_wrap(~Time)+
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Ruminococcaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Akkermansia)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('c 20-24 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Faecalibacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='c20.21', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-24 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__, x=c20.21)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Barnesiellaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop total FA und genus-level
corr_map_genus_total <- filter(genus_FA, !is.na(total))
corr_spearman_genus_total <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_total, !is.na(i))
y = tmp[,i]
x = tmp$total
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$total
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$total
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_total)+1
corr_spearman_genus_total[nrow,"FA"] = "total"
corr_spearman_genus_total[nrow, "Genus"] = i
corr_spearman_genus_total[nrow, "p.value"] = p
corr_spearman_genus_total[nrow, "rho"] = rho
corr_spearman_genus_total[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_total[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_total[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_total[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_total$p.adjusted <- p.adjust(corr_spearman_genus_total$p.value, method = "BH", n = 35)
corr_spearman_genus_total$p.adjusted_PRE <- p.adjust(corr_spearman_genus_total$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_total$p.adjusted_POST <- p.adjust(corr_spearman_genus_total$p.value_POST, method = "BH", n = 35)
corr_sig_genus_total <- filter(corr_spearman_genus_total, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_total, file = '/Users/student05/Documents/fa feces/tabellen/total.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von total FA und genus-level
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') +
ylab('log10 (Relative Abundance g__Oscillospira)')+
facet_wrap(~Time) +
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bifidobacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='total', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Bacteroides)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Sutterella)')+
facet_wrap(~Time)+
theme(text = element_text(size=12),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='total', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Ruminococcaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Akkermansia)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__Faecalibacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Rikenellaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='total', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance f__Rikenellaceae')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__, x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance f__Barnesiellaceae)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus., x=total)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') +
ylab('log10 (Relative Abundance g__.Ruminococcus)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop Omega3 FA und genus-level
corr_map_genus_Omega3 <- filter(genus_FA, !is.na(Omega3))
corr_spearman_genus_Omega3 <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Omega3, !is.na(i))
y = tmp[,i]
x = tmp$Omega3
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Omega3
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Omega3
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Omega3)+1
corr_spearman_genus_Omega3[nrow,"FA"] = "Omega3"
corr_spearman_genus_Omega3[nrow, "Genus"] = i
corr_spearman_genus_Omega3[nrow, "p.value"] = p
corr_spearman_genus_Omega3[nrow, "rho"] = rho
corr_spearman_genus_Omega3[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Omega3[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Omega3[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Omega3[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Omega3$p.adjusted <- p.adjust(corr_spearman_genus_Omega3$p.value, method = "BH", n = 35)
corr_spearman_genus_Omega3$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Omega3$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Omega3$p.adjusted_POST <- p.adjust(corr_spearman_genus_Omega3$p.value_POST, method = "BH", n = 35)
corr_sig_genus_Omega3 <- filter(corr_spearman_genus_Omega3, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_Omega3, file = '/Users/student05/Documents/fa feces/tabellen/Omega3.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von Omega3 FA und genus-level
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [nmol/g]', cor.coef.coord =c(0, -1.9), ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [nmol/g]', cor.coef.coord =c(0, -1.9), ylab = 'log10 (Relative Abundance g__Oscillospira')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Oscillospira',label = 'Proband')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium., x=Omega3)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance g__Eubacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance f__Coriobacteriaceae')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop Omega6 FA und genus-level
corr_map_genus_Omega6 <- filter(genus_FA, !is.na(Omega6))
corr_spearman_genus_Omega6 <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Omega6, !is.na(i))
y = tmp[,i]
x = tmp$Omega6
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Omega6
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Omega6
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Omega6)+1
corr_spearman_genus_Omega6[nrow,"FA"] = "Omega6"
corr_spearman_genus_Omega6[nrow, "Genus"] = i
corr_spearman_genus_Omega6[nrow, "p.value"] = p
corr_spearman_genus_Omega6[nrow, "rho"] = rho
corr_spearman_genus_Omega6[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Omega6[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Omega6[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Omega6[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Omega6$p.adjusted <- p.adjust(corr_spearman_genus_Omega6$p.value, method = "BH", n = 35)
corr_spearman_genus_Omega6$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Omega6$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Omega6$p.adjusted_POST <- p.adjust(corr_spearman_genus_Omega6$p.value_POST, method = "BH", n = 35)
corr_sig_genus_Omega6 <- filter(corr_spearman_genus_Omega6, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_Omega6, file = '/Users/student05/Documents/fa feces/tabellen/Omega6.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
PLotten von Omega6 FA und genus-level
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='Omega6', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance f__Coriobacteriaceae')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop omega6/omega3-ratio und genus-level
corr_map_genus_ratio <- filter(genus_FA, !is.na(ratio))
corr_spearman_genus_ratio <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_ratio, !is.na(i))
y = tmp[,i]
x = tmp$ratio
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$ratio
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$ratio
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_ratio)+1
corr_spearman_genus_ratio[nrow,"FA"] = "ratio"
corr_spearman_genus_ratio[nrow, "Genus"] = i
corr_spearman_genus_ratio[nrow, "p.value"] = p
corr_spearman_genus_ratio[nrow, "rho"] = rho
corr_spearman_genus_ratio[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_ratio[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_ratio[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_ratio[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_ratio$p.adjusted <- p.adjust(corr_spearman_genus_ratio$p.value, method = "BH", n = 35)
corr_spearman_genus_ratio$p.adjusted_PRE <- p.adjust(corr_spearman_genus_ratio$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_ratio$p.adjusted_POST <- p.adjust(corr_spearman_genus_ratio$p.value_POST, method = "BH", n = 35)
corr_sig_genus_ratio <- filter(corr_spearman_genus_ratio, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_ratio, file = '/Users/student05/Documents/fa feces/tabellen/ratio.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
PLotten von omega6/omega3-ratio und genus-level
ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Lachnospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Lachnospira')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Collinsella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop ungesaettigte FA und genus-level
corr_map_genus_unsat <- filter(genus_FA, !is.na(unsat))
corr_spearman_genus_unsat <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_unsat, !is.na(i))
y = tmp[,i]
x = tmp$unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_unsat)+1
corr_spearman_genus_unsat[nrow,"FA"] = "unsat"
corr_spearman_genus_unsat[nrow, "Genus"] = i
corr_spearman_genus_unsat[nrow, "p.value"] = p
corr_spearman_genus_unsat[nrow, "rho"] = rho
corr_spearman_genus_unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_unsat$p.adjusted <- p.adjust(corr_spearman_genus_unsat$p.value, method = "BH", n = 35)
corr_spearman_genus_unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_unsat$p.value_POST, method = "BH", n = 35)
corr_sig_genus_unsat <- filter(corr_spearman_genus_unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_genus_unsat, file = '/Users/student05/Documents/fa feces/tabellen/unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
2.5 Omega-Fa Analysen, Aufnahem ueber Nahrung, Ausscheidung, Ausscheidungsrate , Absorptionsrate, EPA, DHA
Laden der Metadaten und testen auf Normalverteilung
FA_stool.o <- read.table("/Users/student05/Documents/Omega aufnahme 1.txt", sep = '\t', comment='',head=T)
View(FA_stool)
FA_stool.o <- subset(filter(FA_stool.o, !Proband == "33MP"))
FA_colnames.o <- colnames(FA_stool.o[, c(21:23)])
nd.FA.o <- data.frame()
for (i in FA_colnames.o) {
fit <- shapiro.test(as.matrix(as.data.frame(lapply(FA_stool.o[,i],
as.numeric))))
p = fit$p.value
nrow = nrow(nd.FA.o)+1
nd.FA.o[nrow, "column"] = i
nd.FA.o[nrow, "p.value"] = round(p, 4)
}
Plotten der Normalverteilungen
ggqqplot(FA_stool.o$Linolensaeure_f, ylab = "Fecal omega 3 FA concentration [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$Linolsaeure_f, ylab = "Fecal omega 6 FA concentration [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$Linolensaeure_i, ylab = "Intake omega 3 FA concentration [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$EPA_i, ylab = "Intake EPA [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$DHA_i, ylab = "Intake DHA [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$AR_Linolensaeure, ylab = "precipitation rate omega 3 FA concentration [%]", xlab = "SampleID")
ggqqplot(FA_stool.o$AR_Linolsaeure, ylab = "precipitation rate omega 6 FA concentration [%]", xlab = "SampleID")
ggqqplot(FA_stool.o$A_Linolensaeure, ylab = "Intake in the body omega 3 FA [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$A_Linolsaeure, ylab = "Intake in the body omega 6 FA [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$AP_Linolensaeure, ylab = "percentage intake in the body omega 3 FA [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$AP_Linolsaeure, ylab = "percentage intake in the body omega 6 FA [g]", xlab = "SampleID")
Filtern nach PRE und POST
FA_stool_pairs.o <- filter(FA_stool.o, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "31KE" | Proband == "32FG" | Proband == "35AD"| Proband == "36ER"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
FA_stool_pairs.o$Proband
FA_stool_pairs_PP.o <- filter(FA_stool_pairs.o, Time=="PRE" | Time=="POST")
Loop fuer Wilcoxon-test zwischen PRE und POST
wilcox_FA.o<- data_frame()
for (i in FA_colnames.o) {
tmp <- FA_stool_pairs_PP.o %>% drop_na(i)
x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y <- FA_stool_pairs_PP.o$Time
tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = T)
p <- tmp_wilcox$p.value
nrow = nrow(wilcox_FA.o)+1
wilcox_FA.o[nrow, "FA"] <- i
wilcox_FA.o[nrow, "Mean PRE"] <-round(mean(subset(filter(FA_stool_pairs.o,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
wilcox_FA.o[nrow, "sd PRE"] <-round(sd(c(subset(filter(FA_stool_pairs.o,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), na.rm = TRUE)), 4)
wilcox_FA.o[nrow, "Mean POST"] <-round(mean(subset(filter(FA_stool_pairs.o,Time == "POST")[,i],!is.na(i), na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
wilcox_FA.o[nrow, "sd POST"] <- round(sd(c(subset(filter(FA_stool_pairs.o,Time == "POST")[,i],!is.na(i), na.rm = TRUE),na.rm = TRUE)), 4)
wilcox_FA.o[nrow, "p.value"] <- round(p, 4) }
Boxplot der Omega-FA je Zeitpunkt alle FA
FA_stool.melt.o <- melt(FA_stool_pairs.o, id.vars = 'Time', measure.vars = c('Linolensaeure_f', 'Linolsaeure_f', 'Linolensaeure_i', 'Linolsaeure_i'))
FA_stool.melt.o <- rename(FA_stool.melt.o, FA=variable)
FA_stool.melt.o <- rename(FA_stool.melt.o, Concentration=value)
FA_stool.melt.o$Time <- factor(FA_stool.melt.o$Time, levels = c("PRE", "POST"))
ggplot(FA_stool.melt.o,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Time Point') + ylab ('Concentration [g]') +
geom_boxplot() +
scale_fill_manual(labels = c("omega 3 fecal", "omega 6 fecal","omega 3 intake", "omega 6 intake"),
values = c("tomato", "yellowgreen", "steelblue2", "deeppink2")) +
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
Omega 3 in mol
FA_stool.melt.o1 <- melt(FA_stool_pairs.o, id.vars = c('Time','Proband'), measure.vars = c('Linolensaeure_mol'))
FA_stool.melt.o1 <- rename(FA_stool.melt.o1, FA=variable)
FA_stool.melt.o1 <- rename(FA_stool.melt.o1, Concentration=value)
FA_stool.melt.o1$Time <- factor(FA_stool.melt.o1$Time, levels = c("PRE", "POST"))
ggplot(FA_stool.melt.o1,aes(x=Time, y=Concentration, fill= FA),label= 'Proband') +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("omega 3 fecal"),
values = c("tomato")) +
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
geom_text(aes(label=Proband),hjust=0, vjust=0)
pdf("/Users/student05/Documents/fertige Plots/Linolsäure.probands.pdf",width=7.5, height=10)
ggpaired(FA_stool.melt.o1, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('skyblue','orchid4'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'FA', short.panel.labs = FALSE) +
xlab('Fäkale alpha-Linolensäurekonzentrationen [nmol/g]') + ylab('Konzentration [nmol/g]')+
theme(legend.position="top")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))
dev.off()
Omega 6 in mol
FA_stool.melt.o2 <- melt(FA_stool_pairs.o, id.vars = c('Time','Proband'), measure.vars = c('Linolsaeure_mol'))
FA_stool.melt.o2 <- rename(FA_stool.melt.o2, FA=variable)
FA_stool.melt.o2<- rename(FA_stool.melt.o2, Concentration=value)
FA_stool.melt.o2$Time <- factor(FA_stool.melt.o2$Time, levels = c("PRE", "POST"))
ggplot(FA_stool.melt.o2,aes(x=Time, y=Concentration, fill= FA),label= 'Proband') +
xlab ('Time Point') + ylab ('Concentration [nmol/g]') +
geom_boxplot() +
scale_fill_manual(labels = c("omega 6 fecal"),
values = c("yellowgreen")) +
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
geom_text(aes(label=Proband),hjust=0, vjust=0)
ggpaired(FA_stool.melt.o2, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'FA', short.panel.labs = FALSE) +
xlab('fecal omega 6') + ylab('Concentration [nmol/g DW]') +
geom_text(aes(label=Proband),hjust=0, vjust=0)
Omega 3 Aufnahme PRE und POST
FA_stool.melt.o3 <- melt(FA_stool_pairs.o, id.vars = c('Time','Proband'), measure.vars = c('Linolensaeure_i'))
FA_stool.melt.o3 <- rename(FA_stool.melt.o3, FA=variable)
FA_stool.melt.o3 <- rename(FA_stool.melt.o3, Concentration=value)
FA_stool.melt.o3$Time <- factor(FA_stool.melt.o3$Time, levels = c("PRE", "POST"))
ggplot(FA_stool.melt.o3,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Time Point') + ylab ('Concentration [g]') +
geom_boxplot() +
scale_fill_manual(labels = c("omega 3 intake", "omega 6 intake"),
values = c("steelblue2")) +
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
geom_text(aes(label=Proband),hjust=0, vjust=0)
ggpaired(FA_stool.melt.o3, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'FA', short.panel.labs = FALSE) +
xlab('intake omega 3') + ylab('Concentration [nmol/g DW]') +
geom_text(aes(label=Proband),hjust=0, vjust=0)
Omega 6 Aufnahme PRE und POST
FA_stool.melt.o4 <- melt(FA_stool_pairs.o, id.vars = c('Time','Proband'), measure.vars = c('Linolsaeure_i'))
FA_stool.melt.o4 <- rename(FA_stool.melt.o4, FA=variable)
FA_stool.melt.o4 <- rename(FA_stool.melt.o4, Concentration=value)
FA_stool.melt.o4$Time <- factor(FA_stool.melt.o4$Time, levels = c("PRE", "POST"))
ggplot(FA_stool.melt.o4,aes(x=Time, y=Concentration, fill= FA)) +
xlab ('Time Point') + ylab ('Concentration [g]') +
geom_boxplot() +
scale_fill_manual(labels = c("omega 6 intake"),
values = c("deeppink")) +
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
geom_text(aes(label=Proband),hjust=0, vjust=0)
ggpaired(FA_stool.melt.o4, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'FA', short.panel.labs = FALSE) +
xlab('intake omega 6') + ylab('Concentration [nmol/g DW]') +
geom_text(aes(label=Proband),hjust=0, vjust=0)
3.3 Korrelationsanalysen Omega-FA und Taxa Metadaten hochladen, filtern und synchronisieren
relab_means <- read.table('/Users/student05/Documents/relative abundance/relab_means_per_timepoint.txt', sep ='\t', comment='', head=T)
relab_means_melt <- melt(relab_means, id=c('Proband', 'Time'))
relab_means_melt <- dplyr::rename(relab_means_melt, Taxa=variable)
relab_means_melt <- dplyr::rename(relab_means_melt, Relative_Abundance=value)
relab_phylum <- subset(relab_means_melt, !grepl("g__|f__|o__|c__", relab_means_melt$Taxa))
relab_phylum <- subset(relab_phylum, !grepl("k__Archaea", relab_phylum$Taxa))
relab_phylum$Time <- factor(relab_phylum$Time, levels=c('PRE','POST','FOLLOW-UP'))
relab_phylum_spread <- spread(relab_phylum, Taxa, Relative_Abundance, sep = NULL)
relab_genus <- subset(relab_means_melt, grepl("g__", relab_means_melt$Taxa))
relab_genus <- subset(relab_genus, !grepl("k__Archaea", relab_genus$Taxa))
relab_genus$Time <- factor(relab_genus$Time, levels = c('PRE','POST','FOLLOW-UP'))
relab_genus_spread <- spread(relab_genus, Taxa, Relative_Abundance, sep = NULL)
FA_stool.o <- read.table("/Users/student05/Documents/fa feces/FA fecal/omega/Omega aufnahme .txt", sep = '\t', comment='',head=T)
View(FA_stool)
FA_stool.o <- subset(filter(FA_stool.o, !Proband == "33MP"))
relab_phylum_ID <- relab_phylum_spread
relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))
row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID
relab_genus_ID <- relab_genus_spread
relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))
row.names(relab_genus_ID) <- relab_genus_ID$SampleID
FA_stool.o$Proband
FA_stool.o <- subset(filter(FA_stool.o, !Proband == "34WF",!Proband == "49RJ"))
FA_stool.o <- mutate(FA_stool.o, SampleID1 = paste(Proband, Time, sep = "."))
row.names(FA_stool.o) <- FA_stool.o$SampleID1
common.ids.relab <- intersect(rownames(FA_stool.o), rownames(relab_phylum_ID))
FA_stool.o <- FA_stool.o[common.ids.relab,]
relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
Phylum-Level log transformation hinzufuegen von Pseudocount von 0.00001
relab_phylum_ID_log <- relab_phylum_ID[,c(3:8)] + 0.00001
relab_phylum_ID_log <- log10(relab_phylum_ID_log)
phylum_FA <- cbind(relab_phylum_ID_log, FA_stool.o[, c(2:23)])
Loop Korrelation faekale Linolensaeure und phylum-level
corr_map_phylum_omega6f <- filter(phylum_FA, !is.na(Linolsaeure_f))
corr_spearman_Phylum_omega6f <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_omega6f, !is.na(i))
y = tmp[,i]
x = tmp$Linolsaeure_f
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Linolsaeure_f
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Linolsaeure_f
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_omega6f)+1
corr_spearman_Phylum_omega6f[nrow,"FA"] <- "Linoleic fa"
corr_spearman_Phylum_omega6f[nrow, "Phylum"] = i
corr_spearman_Phylum_omega6f[nrow, "p.value"] = p
corr_spearman_Phylum_omega6f[nrow, "rho"] = rho
corr_spearman_Phylum_omega6f[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_omega6f[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_omega6f[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_omega6f[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_omega6f$p.adjusted <- p.adjust(corr_spearman_Phylum_omega6f$p.value, method = "BH", n = 35)
corr_spearman_Phylum_omega6f$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega6f$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_omega6f$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega6f$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_omega6f <- filter(corr_spearman_Phylum_omega6f, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
Plotten von faekaler Omega6 FA und Phylum-level Korrelationen
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=Linolsaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='Linolsaeure_f', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label = 'Proband')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='Linolsaeure_f', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='Linolensaeure_f', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord =c(0, -0.8), xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='Linolensaeure_f', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord =c(0, -0.8), xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='Linolsaeure_f', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='Linolensaeure_f', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=Linolsaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Linolsaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=Linolsaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Linolsaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop Linolensaeure und phylum-level
corr_map_phylum_omega3f <- filter(phylum_FA, !is.na(Linolsaeure_f))
corr_spearman_Phylum_omega3f <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_omega3f, !is.na(i))
y = tmp[,i]
x = tmp$Linolensaeure_f
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Linolensaeure_f
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Linolensaeure_f
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_omega6f)+1
corr_spearman_Phylum_omega3f[nrow,"FA"] <- "Linolenic fa"
corr_spearman_Phylum_omega3f[nrow, "Phylum"] = i
corr_spearman_Phylum_omega3f[nrow, "p.value"] = p
corr_spearman_Phylum_omega3f[nrow, "rho"] = rho
corr_spearman_Phylum_omega3f[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_omega3f[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_omega3f[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_omega3f[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_omega3f$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3f$p.value, method = "BH", n = 35)
corr_spearman_Phylum_omega3f$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3f$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_omega3f$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3f$p.value_POST, method = "BH", n = 35)
Plotten von Korrelationen zwischen Omega3-FA und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=Linolensaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='Linolensaeure_f', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label='Proband')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=Linolensaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Linolensaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop Omega6 Aufnahme und phylum-level
corr_map_phylum_omega6i <- filter(phylum_FA, !is.na(Linolsaeure_i))
corr_spearman_Phylum_omega6i <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_omega6i, !is.na(i))
y = tmp[,i]
x = tmp$Linolsaeure_i
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Linolsaeure_i
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Linolsaeure_i
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_omega6i)+1
corr_spearman_Phylum_omega6i[nrow,"FA"] <- "Linoleic fa i"
corr_spearman_Phylum_omega6i[nrow, "Phylum"] = i
corr_spearman_Phylum_omega6i[nrow, "p.value"] = p
corr_spearman_Phylum_omega6i[nrow, "rho"] = rho
corr_spearman_Phylum_omega6i[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_omega6i[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_omega6i[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_omega6i[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_omega6i$p.adjusted <- p.adjust(corr_spearman_Phylum_omega6i$p.value, method = "BH", n = 35)
corr_spearman_Phylum_omega6i$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega6i$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_omega6i$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega6i$p.value_POST, method = "BH", n = 35)
Plotten von Korrelationen zwischen Omega6 Aufnahme und phylum-level
phylum_FA$Time <- factor(phylum_FA$Time, levels = c("PRE", "POST"))
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Linolsaeure_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='Linolsaeure_i', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration intake [g]', ylab = 'log10 (Relative Abundance p__Actinobacteria)', label = 'Proband')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=Linolsaeure_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=Linolsaeure_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop Omega3 Aufnahme und phylum-level
corr_map_phylum_omega3i <- filter(phylum_FA, !is.na(Linolensaeure_i))
corr_spearman_Phylum_omega3i <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_omega3i, !is.na(i))
y = tmp[,i]
x = tmp$Linolensaeure_i
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Linolensaeure_i
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Linolensaeure_i
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_omega3i)+1
corr_spearman_Phylum_omega3i[nrow,"FA"] <- "Linolenic fa i"
corr_spearman_Phylum_omega3i[nrow, "Phylum"] = i
corr_spearman_Phylum_omega3i[nrow, "p.value"] = p
corr_spearman_Phylum_omega3i[nrow, "rho"] = rho
corr_spearman_Phylum_omega3i[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_omega3i[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_omega3i[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_omega3i[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_omega3i$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3i$p.value, method = "BH", n = 35)
corr_spearman_Phylum_omega3i$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3i$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_omega3i$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3i$p.value_POST, method = "BH", n = 35)
Plotten Omega3 Aufnahme und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Linolensaeure_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='Linolensaeure_i', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration intake [g]', ylab = 'log10 (Relative Abundance p__Actinobacteria)',label = 'Proband')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=Linolensaeure_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=Linolensaeure_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop Omega3 FA Absorption in g
corr_map_phylum_omega3a <- filter(phylum_FA, !is.na(A_Linolensaeure))
corr_spearman_Phylum_omega3a <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_omega3a, !is.na(i))
y = tmp[,i]
x = tmp$A_Linolensaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$A_Linolensaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$A_Linolensaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_omega3a)+1
corr_spearman_Phylum_omega3a[nrow,"FA"] <- "Linolenic fa body i"
corr_spearman_Phylum_omega3a[nrow, "Phylum"] = i
corr_spearman_Phylum_omega3a[nrow, "p.value"] = p
corr_spearman_Phylum_omega3a[nrow, "rho"] = rho
corr_spearman_Phylum_omega3a[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_omega3a[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_omega3a[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_omega3a[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_omega3a$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3a$p.value, method = "BH", n = 35)
corr_spearman_Phylum_omega3a$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3a$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_omega3a$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3a$p.value_POST, method = "BH", n = 35)
Plotten von Omega3 Aufnahme in g und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=A_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=A_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=A_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop Omega3 Ausscheidungsrate in Prozent und phylum-level
corr_map_phylum_omega3ar <- filter(phylum_FA, !is.na(AR_Linolensaeure))
corr_spearman_Phylum_omega3ar <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_omega3ar, !is.na(i))
y = tmp[,i]
x = tmp$AR_Linolensaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$AR_Linolensaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$AR_Linolensaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_omega3ar)+1
corr_spearman_Phylum_omega3ar[nrow,"FA"] <- "Linolenic fa Precipitation"
corr_spearman_Phylum_omega3ar[nrow, "Phylum"] = i
corr_spearman_Phylum_omega3ar[nrow, "p.value"] = p
corr_spearman_Phylum_omega3ar[nrow, "rho"] = rho
corr_spearman_Phylum_omega3ar[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_omega3ar[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_omega3ar[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_omega3ar[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_omega3ar$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3ar$p.value, method = "BH", n = 35)
corr_spearman_Phylum_omega3ar$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3ar$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_omega3ar$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3ar$p.value_POST, method = "BH", n = 35)
Plotten Omega3 Ausscheidungsrate in Prozent und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=AR_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=AR_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=AR_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=AR_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=AR_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop Omega6 Ausscheidungsrate in Prozent und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=AR_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=AR_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label= 'Proband')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=AR_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=AR_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=AR_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label = 'Proband')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=AR_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop Omega6 Absorption in Prozent und phylum-level
corr_map_phylum_omega6ap <- filter(phylum_FA, !is.na(AP_Linolsaeure))
corr_spearman_Phylum_omega6ap <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_omega6ap, !is.na(i))
y = tmp[,i]
x = tmp$AP_Linolsaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$AP_Linolsaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$AP_Linolsaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_omega6ap)+1
corr_spearman_Phylum_omega6ap[nrow,"FA"] <- "Linoleic fa i [%]"
corr_spearman_Phylum_omega6ap[nrow, "Phylum"] = i
corr_spearman_Phylum_omega6ap[nrow, "p.value"] = p
corr_spearman_Phylum_omega6ap[nrow, "rho"] = rho
corr_spearman_Phylum_omega6ap[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_omega6ap[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_omega6ap[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_omega6ap[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_omega6ap$p.adjusted <- p.adjust(corr_spearman_Phylum_omega6ap$p.value, method = "BH", n = 35)
corr_spearman_Phylum_omega6ap$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega6ap$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_omega6ap$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega6ap$p.value_POST, method = "BH", n = 35)
Plotten von Omega6 Absorption in Prozent und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=AP_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=AP_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=AP_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=AP_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=AP_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=AR_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop Omega3 Absorption in Prozent und phylum-level
corr_map_phylum_omega3ap <- filter(phylum_FA, !is.na(AP_Linolensaeure))
corr_spearman_Phylum_omega3ap <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_omega3ap, !is.na(i))
y = tmp[,i]
x = tmp$AP_Linolensaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$AP_Linolensaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$AP_Linolensaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_omega3ap)+1
corr_spearman_Phylum_omega3ap[nrow,"FA"] <- "Linolenic fa i [%]"
corr_spearman_Phylum_omega3ap[nrow, "Phylum"] = i
corr_spearman_Phylum_omega3ap[nrow, "p.value"] = p
corr_spearman_Phylum_omega3ap[nrow, "rho"] = rho
corr_spearman_Phylum_omega3ap[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_omega3ap[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_omega3ap[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_omega3ap[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_omega3ap$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3ap$p.value, method = "BH", n = 35)
corr_spearman_Phylum_omega3ap$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3ap$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_omega3ap$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3ap$p.value_POST, method = "BH", n = 35)
Plotten von Omega3 Absorption in Prozent und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=AP_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=AP_Linolensaeure)) + geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid intake rate [%]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=AP_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=AP_Linolensaeure)) + geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=AP_Linolensaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid intake rate [%]') +
ylab('log10 (Relative Abundance p__Bacteroidetes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=AR_Linolsaeure)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop EPA und phylum-level
corr_map_phylum_epa <- filter(phylum_FA, !is.na(EPA_i))
corr_spearman_Phylum_epa <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_epa, !is.na(i))
y = tmp[,i]
x = tmp$EPA_i
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$EPA_i
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$EPA_i
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_epa)+1
corr_spearman_Phylum_epa[nrow,"FA"] <- "EPA intake [g]"
corr_spearman_Phylum_epa[nrow, "Phylum"] = i
corr_spearman_Phylum_epa[nrow, "p.value"] = p
corr_spearman_Phylum_epa[nrow, "rho"] = rho
corr_spearman_Phylum_epa[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_epa[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_epa[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_epa[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_epa$p.adjusted <- p.adjust(corr_spearman_Phylum_epa$p.value, method = "BH", n = 35)
corr_spearman_Phylum_epa$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_epa$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_epa$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_epa$p.value_POST, method = "BH", n = 35)
Plotten von EPA und phylum-level
ggscatter(phylum_FA, x='EPA_i', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label= 'Proband')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=EPA_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('EPA intake [g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=EPA_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('EPA intake [g]') +
ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=EPA_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('EPA intake [g]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Loop DHA und phylum-level
corr_map_phylum_dha <- filter(phylum_FA, !is.na(DHA_i))
corr_spearman_Phylum_dha <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_dha, !is.na(i))
y = tmp[,i]
x = tmp$DHA_i
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$DHA_i
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$DHA_i
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_dha)+1
corr_spearman_Phylum_dha[nrow,"FA"] <- "DHA intake [g]"
corr_spearman_Phylum_dha[nrow, "Phylum"] = i
corr_spearman_Phylum_dha[nrow, "p.value"] = p
corr_spearman_Phylum_dha[nrow, "rho"] = rho
corr_spearman_Phylum_dha[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_dha[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_dha[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_dha[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_dha$p.adjusted <- p.adjust(corr_spearman_Phylum_dha$p.value, method = "BH", n = 35)
corr_spearman_Phylum_dha$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_dha$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_dha$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_dha$p.value_POST, method = "BH", n = 35)
Plotten von DHA und phylum-level Korrelationen
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=DHA_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('DHA intake [g]') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=DHA_i)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('DHA intake [g]') +
ylab('log10 (Relative Abundance p__Tenericutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggscatter(phylum_FA, x='DHA_i', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label= 'Proband')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop faekales Omega6/Omega3-ratio und phylum-level
corr_map_phylum_ra <- filter(phylum_FA, !is.na(ratio_f))
corr_spearman_Phylum_ra <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_ra, !is.na(i))
y = tmp[,i]
x = tmp$ratio_f
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$ratio_f
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$ratio_f
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_ra)+1
corr_spearman_Phylum_ra[nrow,"FA"] <- "ratio omega6/omega3 fecal"
corr_spearman_Phylum_ra[nrow, "Phylum"] = i
corr_spearman_Phylum_ra[nrow, "p.value"] = p
corr_spearman_Phylum_ra[nrow, "rho"] = rho
corr_spearman_Phylum_ra[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_ra[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_ra[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_ra[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_ra$p.adjusted <- p.adjust(corr_spearman_Phylum_ra$p.value, method = "BH", n = 35)
corr_spearman_Phylum_ra$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_ra$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_ra$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_ra$p.value_POST, method = "BH", n = 35)
Plotten von faekalem Omega6/Omega3-ratio und phylum-level
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=ratio_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') +
ylab('log10 (Relative Abundance p__Actinobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=ratio_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') +
ylab('log10 (Relative Abundance p__Proteobacteria)')+
facet_wrap(~Time)+
theme(legend.position="top")
ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=ratio_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') +
ylab('log10 (Relative Abundance p__Firmicutes)')+
facet_wrap(~Time)+
theme(legend.position="top")
Genus-level, Filtern der Metadaten, log transformation und hinzufuegen von Pseudocount 0.00001
genus_colnames <- colnames(relab_genus_spread[, c(3:31)])
relab_genus_ID_log <- relab_genus_ID[,c(3:31)] + 0.00001
relab_genus_ID_log <- log10(relab_genus_ID_log)
genus_FA <- cbind(relab_genus_ID_log, FA_stool.o)
genus_FA$Time <- factor(genus_FA$Time, levels = c("PRE", "POST"))
Loop faekale Omega3 FA und genus-level
corr_map_genus_omega3f <- filter(genus_FA, !is.na(Linolensaeure_f))
corr_spearman_genus_omega3f <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omega3f, !is.na(i))
y = tmp[,i]
x = tmp$Linolensaeure_f
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Linolensaeure_f
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Linolensaeure_f
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omega3f)+1
corr_spearman_genus_omega3f[nrow,"FA"] = "Linolenic fa fecal"
corr_spearman_genus_omega3f[nrow, "Genus"] = i
corr_spearman_genus_omega3f[nrow, "p.value"] = p
corr_spearman_genus_omega3f[nrow, "rho"] = rho
corr_spearman_genus_omega3f[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omega3f[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omega3f[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omega3f[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omega3f$p.adjusted <- p.adjust(corr_spearman_genus_omega3f$p.value, method = "BH", n = 35)
corr_spearman_genus_omega3f$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega3f$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omega3f$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3f$p.value_POST, method = "BH", n = 35)
Plotten faekale Omega3 FA und genus-level
ggscatter(genus_FA, x='Linolensaeure_mol', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [nmol/g]', cor.coef.coord =c(0, -1.9), ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='Linolensaeure_mol', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [nmol/g]', cor.coef.coord =c(0, -1.9), ylab = 'log10 (Relative Abundance g__Oscillospira')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Oscillospira',label = 'Proband')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium., x=Linolensaeure_f)) +
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) +
geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') +
ylab('log10 (Relative Abundance g__Eubacterium)')+
facet_wrap(~Time)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance f__Coriobacteriaceae')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop faekale Omega6-FA und genus-level
corr_map_genus_omega6f <- filter(genus_FA, !is.na(Linolsaeure_f))
corr_spearman_genus_omega6f <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omega6f, !is.na(i))
y = tmp[,i]
x = tmp$Linolsaeure_f
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Linolensaeure_f
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Linolsaeure_f
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omega6f)+1
corr_spearman_genus_omega6f[nrow,"FA"] = "Linoleic fa fecal"
corr_spearman_genus_omega6f[nrow, "Genus"] = i
corr_spearman_genus_omega6f[nrow, "p.value"] = p
corr_spearman_genus_omega6f[nrow, "rho"] = rho
corr_spearman_genus_omega6f[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omega6f[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omega6f[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omega6f[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omega6f$p.adjusted <- p.adjust(corr_spearman_genus_omega6f$p.value, method = "BH", n = 35)
corr_spearman_genus_omega6f$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega6f$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omega6f$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega6f$p.value_POST, method = "BH", n = 35)
Plotten faekale Omega6-FA und genus-level
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop prozentuale Ausscheidungsrate Omega3-FA und genus-level
corr_map_genus_omega3ar <- filter(genus_FA, !is.na(AR_Linolensaeure))
corr_spearman_genus_omega3ar <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omega3ar, !is.na(i))
y = tmp[,i]
x = tmp$AR_Linolensaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$AR_Linolensaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$AR_Linolensaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omega3ar)+1
corr_spearman_genus_omega3ar[nrow,"FA"] = "Linolenic fa Precipitation rate "
corr_spearman_genus_omega3ar[nrow, "Genus"] = i
corr_spearman_genus_omega3ar[nrow, "p.value"] = p
corr_spearman_genus_omega3ar[nrow, "rho"] = rho
corr_spearman_genus_omega3ar[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omega3ar[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omega3ar[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omega3ar[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omega3ar$p.adjusted <- p.adjust(corr_spearman_genus_omega3ar$p.value, method = "BH", n = 35)
corr_spearman_genus_omega3ar$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega3ar$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omega3ar$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3ar$p.value_POST, method = "BH", n = 35)
Plotten prozentuale Ausscheidungsrate Omega3-FA und genus-level
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Blautia')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop prozentuale Ausscheidungsrate Omega6-FA und genus-level
corr_map_genus_omega6ar <- filter(genus_FA, !is.na(AR_Linolsaeure))
corr_spearman_genus_omega6ar <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omega6ar, !is.na(i))
y = tmp[,i]
x = tmp$AR_Linolsaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$AR_Linolsaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$AR_Linolsaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omega6ar)+1
corr_spearman_genus_omega6ar[nrow,"FA"] = "Linoleic fa Precipitation rate "
corr_spearman_genus_omega6ar[nrow, "Genus"] = i
corr_spearman_genus_omega6ar[nrow, "p.value"] = p
corr_spearman_genus_omega6ar[nrow, "rho"] = rho
corr_spearman_genus_omega6ar[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omega6ar[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omega6ar[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omega6ar[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omega6ar$p.adjusted <- p.adjust(corr_spearman_genus_omega6ar$p.value, method = "BH", n = 35)
corr_spearman_genus_omega6ar$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega6ar$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omega6ar$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega6ar$p.value_POST, method = "BH", n = 35)
Plotten prozentuale Omega6 Ausscheidungsrate und genus-level
ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Prevotella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Blautia')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance f__Rikenellaceae')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop absorbierte Omega3-FA in g und genus-level
corr_map_genus_omega3a <- filter(genus_FA, !is.na(A_Linolensaeure))
corr_spearman_genus_omega3a <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omega3a, !is.na(i))
y = tmp[,i]
x = tmp$A_Linolensaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$A_Linolensaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$A_Linolensaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omega3a)+1
corr_spearman_genus_omega3a[nrow,"FA"] = "Linolenic fa intake into the body "
corr_spearman_genus_omega3a[nrow, "Genus"] = i
corr_spearman_genus_omega3a[nrow, "p.value"] = p
corr_spearman_genus_omega3a[nrow, "rho"] = rho
corr_spearman_genus_omega3a[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omega3a[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omega3a[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omega3a[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omega3a$p.adjusted <- p.adjust(corr_spearman_genus_omega3a$p.value, method = "BH", n = 35)
corr_spearman_genus_omega3a$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega3a$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omega3a$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3a$p.value_POST, method = "BH", n = 35)
Plotten absorbierte Omega3-FA in g und genus-level
ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Prevotella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance f__Barnesiellaceae')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Lachnospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Lachnospira')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop absorbierte Omega6-FA in g und genus-level
corr_map_genus_omega6a <- filter(genus_FA, !is.na(A_Linolsaeure))
corr_spearman_genus_omega6a <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omega6a, !is.na(i))
y = tmp[,i]
x = tmp$A_Linolsaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$A_Linolsaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$A_Linolsaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omega6a)+1
corr_spearman_genus_omega6a[nrow,"FA"] = "Linoleic fa intake into the body "
corr_spearman_genus_omega6a[nrow, "Genus"] = i
corr_spearman_genus_omega6a[nrow, "p.value"] = p
corr_spearman_genus_omega6a[nrow, "rho"] = rho
corr_spearman_genus_omega6a[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omega6a[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omega6a[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omega6a[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omega6a$p.adjusted <- p.adjust(corr_spearman_genus_omega6a$p.value, method = "BH", n = 35)
corr_spearman_genus_omega6a$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega6a$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omega6a$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega6a$p.value_POST, method = "BH", n = 35)
Plotten absorbierte Omega6-FA in g und genus-level
ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absiorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absiorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absiorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Prevotella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop prozentuale Omega3-FA Absorption und genus-level
corr_map_genus_omega3ap <- filter(genus_FA, !is.na(AP_Linolensaeure))
corr_spearman_genus_omega3ap <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omega3ap, !is.na(i))
y = tmp[,i]
x = tmp$AP_Linolensaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$AP_Linolensaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$AP_Linolensaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omega3ap)+1
corr_spearman_genus_omega3ap[nrow,"FA"] = "Linolenic fa intake into the body [%] "
corr_spearman_genus_omega3ap[nrow, "Genus"] = i
corr_spearman_genus_omega3ap[nrow, "p.value"] = p
corr_spearman_genus_omega3ap[nrow, "rho"] = rho
corr_spearman_genus_omega3ap[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omega3ap[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omega3ap[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omega3ap[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omega3ap$p.adjusted <- p.adjust(corr_spearman_genus_omega3ap$p.value, method = "BH", n = 35)
corr_spearman_genus_omega3ap$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega3ap$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omega3ap$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3ap$p.value_POST, method = "BH", n = 35)
Plotten prozentuale Omega3-FA Absorption und genus-level
ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance f__Lachnospiraceae')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop prozentuale Omega6-FA Absorption und genus-level
corr_map_genus_omega6ap <- filter(genus_FA, !is.na(AP_Linolsaeure))
corr_spearman_genus_omega6ap <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omega6ap, !is.na(i))
y = tmp[,i]
x = tmp$AP_Linolsaeure
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$AP_Linolsaeure
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$AP_Linolsaeure
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omega6ap)+1
corr_spearman_genus_omega6ap[nrow,"FA"] = "Linoleic fa intake into the body [%] "
corr_spearman_genus_omega6ap[nrow, "Genus"] = i
corr_spearman_genus_omega6ap[nrow, "p.value"] = p
corr_spearman_genus_omega6ap[nrow, "rho"] = rho
corr_spearman_genus_omega6ap[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omega6ap[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omega6ap[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omega6ap[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omega6ap$p.adjusted <- p.adjust(corr_spearman_genus_omega6ap$p.value, method = "BH", n = 35)
corr_spearman_genus_omega6ap$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega6ap$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omega3ap$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3ap$p.value_POST, method = "BH", n = 35)
Plotten prozentuale Omega6-FA Absorption und genus-level
ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop EPA-Aufnahme und genus-level
corr_map_genus_omegaepa <- filter(genus_FA, !is.na(EPA_i))
corr_spearman_genus_omegaepa <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omegaepa, !is.na(i))
y = tmp[,i]
x = tmp$EPA_i
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$EPA_i
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$EPA_i
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omegaepa)+1
corr_spearman_genus_omegaepa[nrow,"FA"] = "EPA intake [g] "
corr_spearman_genus_omegaepa[nrow, "Genus"] = i
corr_spearman_genus_omegaepa[nrow, "p.value"] = p
corr_spearman_genus_omegaepa[nrow, "rho"] = rho
corr_spearman_genus_omegaepa[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omegaepa[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omegaepa[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omegaepa[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omegaepa$p.adjusted <- p.adjust(corr_spearman_genus_omegaepa$p.value, method = "BH", n = 35)
corr_spearman_genus_omegaepa$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omegaepa$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omegaepa$p.adjusted_POST <- p.adjust(corr_spearman_genus_omegaepa$p.value_POST, method = "BH", n = 35)
Plotten von EPA-Aufnahme und genus-level
ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop DHA-Aufnahme und genus-level
corr_map_genus_omegadha <- filter(genus_FA, !is.na(DHA_i))
corr_spearman_genus_omegadha <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_omegadha, !is.na(i))
y = tmp[,i]
x = tmp$DHA_i
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$DHA_i
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$DHA_i
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_omegadha)+1
corr_spearman_genus_omegadha[nrow,"FA"] = "DHA intake [g] "
corr_spearman_genus_omegadha[nrow, "Genus"] = i
corr_spearman_genus_omegadha[nrow, "p.value"] = p
corr_spearman_genus_omegadha[nrow, "rho"] = rho
corr_spearman_genus_omegadha[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_omegadha[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_omegadha[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_omegadha[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_omegadha$p.adjusted <- p.adjust(corr_spearman_genus_omegadha$p.value, method = "BH", n = 35)
corr_spearman_genus_omegadha$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omegadha$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_omegadha$p.adjusted_POST <- p.adjust(corr_spearman_genus_omegadha$p.value_POST, method = "BH", n = 35)
Plotten DHA-Aufnahme und genus-level
ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Blautia')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Loop fäkales Omega6/Omega3-ratio und genus-level
corr_map_genus_ra <- filter(genus_FA, !is.na(ratio_f))
corr_spearman_genus_ra <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_ra, !is.na(i))
y = tmp[,i]
x = tmp$ratio_f
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$ratio_f
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$ratio_f
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_ra)+1
corr_spearman_genus_ra[nrow,"FA"] = "omega6/omega3 ratio "
corr_spearman_genus_ra[nrow, "Genus"] = i
corr_spearman_genus_ra[nrow, "p.value"] = p
corr_spearman_genus_ra[nrow, "rho"] = rho
corr_spearman_genus_ra[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_ra[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_ra[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_ra[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_ra$p.adjusted <- p.adjust(corr_spearman_genus_ra$p.value, method = "BH", n = 35)
corr_spearman_genus_ra$p.adjusted_PRE <- p.adjust(corr_spearman_genus_ra$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_ra$p.adjusted_POST <- p.adjust(corr_spearman_genus_ra$p.value_POST, method = "BH", n = 35)
Plotten Omega6/Omega3-ratio und genus-level
ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Lachnospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Lachnospira')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Collinsella')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
- Sättigungstypen Einteilung der Sättigungstypen nach fäkaler Fettsäuresättigung: sat, unsat, change.sat, change.unsat Laden der Metadaten Vergleich der Aufnahme von Fettsäuren über Nahrung zwischen den Sättigungstypen
FA_stool.ST <- read.table("/Users/student05/Documents/fa saturation mit intake types.txt", sep = '\t', comment='',
head=T)
View(FA_stool.ST)
FA_stool.ST$Time <-factor(FA_stool.ST$Time, levels = c("PRE", "POST"))
row.names(FA_stool.ST) <- FA_stool.ST$SampleID
FA_stool.ST$Proband
FA_stool.ST <- subset(filter(FA_stool.ST, !SampleID == "ST.35AD.0U1"))
FA_stool.ST <- subset(filter(FA_stool.ST, !Proband == "33MP", !Proband == "35AD", !Proband == "34WF", !Proband == "49RJ"))
comparison_sat <- list(c("sat", "unsat"))
comparison_change <- list(c("change.sat", "change.unsat"))
comparison_time <- list(c("PRE", "POST"))
Korrelationen durch die Nahrung aufgenommene ungesättigte FA mit fäkalen ungesättigten FA
stool.melt.unsat <- melt(FA_stool.ST, id.vars = c('Time','Proband'), measure.vars = c('unsat', 'unsat.i'))
stool.melt.unsat<- dplyr::rename(stool.melt.unsat, FA=variable)
stool.melt.unsat<- dplyr::rename(stool.melt.unsat, Concentration=value)
stool.melt.unsat$Time <- factor(stool.melt.unsat$Time, levels = c("PRE", "POST"))
ggpaired(stool.melt.unsat, x='FA', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, facet.by = 'Time', short.panel.labs = FALSE) +
xlab('unsaturated fatty acids fecal and intake') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='unsat', y='unsat.i',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'Unsaturated fatty acids concentrations intake [g]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='unsat', y='unsat.i', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'Unsaturated fatty acids concentrations intake [g]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Korrelationen durch die Nahrung aufgenommene gesättigte FA mit fäkalen gesättigten FA
stool.melt.sat <- melt(FA_stool.ST, id.vars = c('Time','Proband'), measure.vars = c('sat', 'sat.i'))
stool.melt.sat<- dplyr::rename(stool.melt.sat, FA=variable)
stool.melt.sat<- dplyr::rename(stool.melt.sat, Concentration=value)
stool.melt.sat$Time <- factor(stool.melt.sat$Time, levels = c("PRE", "POST"))
ggpaired(stool.melt.sat, x='FA', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, facet.by = 'Time', short.panel.labs = FALSE) +
xlab('saturated fatty acids fecal and intake') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='sat', y='sat.i',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'Saturated fatty acids concentrations intake [g]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='sat', y='sat.i', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'Saturated fatty acids concentrations intake [g]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Wilcoxon-Test zwischen Sättigungstypen und Fettsäureaufnahme durch Nahrung
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$sat.i, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$sat.i, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$unsat.i, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$unsat.i, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$sat.i, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$sat.i, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$sat.i, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$sat.i, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$unsat.i, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$unsat.i, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$unsat.i, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$unsat.i, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)
Plotten der Unterschiede zwischen den Sättigungstypen
FA_stool.ST$Time <- factor(FA_stool.ST$Time, levels = c("PRE", "POST"))
ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=sat.i)) + xlab('Phenotype') + ylab('Intake Saturated fatty acid Concentration [g]') +
geom_boxplot(fill='whitesmoke', color='black') +
geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=unsat.i)) + xlab('Phenotype') + ylab('Intake unsaturated fatty acid Concentration [g]') +
geom_boxplot(fill='whitesmoke', color='black') +
geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=sat.i)) + xlab('Time Point') +
ylab('Intake saturated fatty acid Concentration [g]') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)
ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=unsat.i)) + xlab('Time Point') +
ylab('Intake unaturated fatty acid Concentration [g]') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)
Bestimmen der HDL und LDL Konzentrationen und Ratio der Sättigungstypen
Plotten von Korrelation zwischen LDL und fäkalen gesättigten FA
ggscatter(FA_stool.ST, x='sat', y='LDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL Concentration [mg/dl]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='sat', y='LDL', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL Concentration [mg/dl]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen HDL und fäkalen gesättigten FA
ggscatter(FA_stool.ST, x='sat', y='HDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'HDL Concentration [mg/dl]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='sat', y='HDL', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'HDL Concentration [mg/dl]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen HDL/LDL-ratio und fäkalen gesättigten FA
ggscatter(FA_stool.ST, x='sat', y='LDL_HDL_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL/HDL ratio')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='sat', y='LDL_HDL_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL/HDL ratio')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen LDL und fäkalen ungesättigten FA
ggscatter(FA_stool.ST, x='unsat', y='LDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL Concentration [mg/dl]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='unsat', y='LDL', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL Concentration [mg/dl]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen HDL und fäkalen ungesättigten FA
ggscatter(FA_stool.ST, x='unsat', y='HDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'HDL Concentration [mg/dl]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='unsat', y='HDL', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'HDL Concentration [mg/dl]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen HDL/LDL-ratio und fäkalen ungesättigten FA
ggscatter(FA_stool.ST, x='unsat', y='LDL_HDL_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL/HDL ratio')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='unsat', y='LDL_HDL_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL/HDL ratio')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen LDL und aufgenommenen gesättigten FA
ggscatter(FA_stool.ST, x='sat.i', y='LDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'LDL Concentration [mg/dl]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='sat.i', y='LDL', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'LDL Concentration [mg/dl]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen HDL und aufgenommenen gesättigten FA
ggscatter(FA_stool.ST, x='sat.i', y='HDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'HDL Concentration [mg/dl]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='sat.i', y='HDL', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'HDL Concentration [mg/dl]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen LDL/HDL-ratio und aufgenommenen gesättigten FA
ggscatter(FA_stool.ST, x='sat.i', y='LDL_HDL_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'LDL/HDL ratio')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='sat.i', y='LDL_HDL_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'LDL/HDL ratio')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen LDL und aufgenommenen ungesättigten FA
ggscatter(FA_stool.ST, x='unsat.i', y='LDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'LDL Concentration [mg/dl]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='unsat.i', y='LDL', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'LDL Concentration [mg/dl]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen HDL und aufgenommenen ungesättigten FA
ggscatter(FA_stool.ST, x='unsat.i', y='HDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'HDL Concentration [mg/dl]')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='unsat.i', y='HDL', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'HDL Concentration [mg/dl]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Plotten von Korrelationen zwischen LDL/HDL-ratio und aufgenommenen ungesättigten FA
ggscatter(FA_stool.ST, x='unsat.i', y='LDL_HDL_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'LDL/HDL ratio')+
facet_grid(.~ Time, scales="free")+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(FA_stool.ST, x='unsat.i', y='LDL_HDL_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'LDL/HDL ratio')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
Wilcoxon-Test Unterschiede in LDL und HDL-Konzentrationen zwischen den Sättigungstypen
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$LDL, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$LDL, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$HDL, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$HDL, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$LDL, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$LDL, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$LDL, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$LDL, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$HDL, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$HDL, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$HDL, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$HDL, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)
Plotten der Unterschiede
FA_stool.ST$Time <- factor(FA_stool.ST$Time, levels = c("PRE", "POST"))
ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=LDL)) + xlab('Phenotype') + ylab('LDL concentration [mg/dl]') +
geom_boxplot(fill='whitesmoke', color='black') +
geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=HDL)) + xlab('Phenotype') + ylab('HDL concentration [mg/dl]') +
geom_boxplot(fill='whitesmoke', color='black') +
geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=LDL_HDL_ratio)) + xlab('Phenotype') + ylab('LDL/HDL ratio') +
geom_boxplot(fill='whitesmoke', color='black') +
geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=LDL)) + xlab('Time Point') +
ylab('LDL concentration [mg/dl]') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)
ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=HDL)) + xlab('Time Point') +
ylab('HDL concentration [mg/dl]') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)
ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=LDL_HDL_ratio)) + xlab('Time Point') +
ylab('LDL/HDL ratio') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)
Testen der signifikanten Unterschiede der Fettsäurenausscheidung der Sättigungstypen Laden und filtern der Metadaten
FA_stool.ST <- read.table("/Users/student05/Documents/fa saturation mit intake types.txt", sep = '\t', comment='',
head=T)
View(FA_stool)
FA_stool.ST$Time <-factor(FA_stool.ST$Time, levels = c("PRE", "POST"))
row.names(FA_stool.ST) <- FA_stool.ST$SampleID
FA_stool.ST$Proband
FA_stool.ST <- subset(filter(FA_stool.ST, !SampleID == "ST.35AD.0U1"))
FA_stool.ST <- subset(filter(FA_stool.ST, !Proband == "33MP", !Proband == "35AD", !Proband == "34WF", !Proband == "49RJ"))
comparison_sat <- list(c("sat", "unsat"))
comparison_change <- list(c("change.sat", "change.unsat"))
comparison_time <- list(c("PRE", "POST"))
Wilcoxon Test Unterschiede fäkaler Fettsäurekonzentrationen zwischen Sättigungstypen
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$sat, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$sat, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$unsat, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$unsat, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$sat, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$sat, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$sat, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$sat, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)
Plotten der Unterschiede
ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=sat)) + xlab('Phenotype') + ylab('Saturated fatty acid Concentration [nmol/g DW]') +
geom_boxplot(fill='whitesmoke', color='black') +
geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=unsat)) + xlab('Phenotype') + ylab('Unsaturated fatty acid Concentration [nmol/g DW]') +
geom_boxplot(fill='whitesmoke', color='black') +
geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=sat)) + xlab('Time Point') +
ylab('Saturated fatty acid Concentration [nmol/g DW]') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)
ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=unsat)) + xlab('Time Point') +
ylab('Unaturated fatty acid Concentration [nmol/g DW]') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)
Bestimmen der means und SD
mean(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "sat"))$sat)
sd(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "sat"))$sat)
mean(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "sat"))$sat)
sd(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "sat"))$sat)
mean(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "unsat"))$sat)
sd(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "unsat"))$sat)
mean(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "unsat"))$sat)
sd(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "unsat"))$sat)
mean(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "sat"))$unsat)
sd(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "sat"))$unsat)
mean(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "sat"))$unsat)
sd(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "sat"))$unsat)
mean(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "unsat"))$unsat)
sd(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "unsat"))$unsat)
mean(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "unsat"))$unsat)
sd(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "unsat"))$unsat)
Testen von Unterschiede im relativen Vorkommen der Taxa zwischen den Sättigungstypen Laden der Phylum-Metadaten s.o. Synchronisieren der Daten, hinzufügen von log-Transformation und Pseudocount 0.00001
relab_phylum_ID <- relab_phylum_spread
relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))
row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID
relab_genus_ID <- relab_genus_spread
relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))
row.names(relab_genus_ID) <- relab_genus_ID$SampleID
FA_stool <- subset(filter(FA_stool, !Proband == "31KE", !Proband == "34WF",
!Proband == "45GL", !Proband == "49RJ", !Proband == "54SL", !Proband == "74SA"))
FA_stool.ST <- mutate(FA_stool.ST, SampleID1 = paste(Proband, Time, sep = "."))
row.names(FA_stool.ST) <- FA_stool.ST$SampleID1
common.ids.relab <- intersect(rownames(FA_stool.ST), rownames(relab_phylum_ID))
FA_stool.ST <- FA_stool.ST[common.ids.relab,]
relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
relab_phylum_ID1 <- relab_phylum_ID[,c(3:8)] + 0.00001
relab_phylum_ID_log <- log10(relab_phylum_ID_log)
phylum_ST <- cbind(relab_phylum_ID1, FA_stool.ST)
write.table(phylum_ST, file = '/Users/student05/Documents/fa feces/FA fecal/saturation types/phylum-phenotype.txt', sep = "\t", col.names = TRUE,row.names = FALSE)
Wilcoxon-Test zur Bestimmung von Unterschiede im relativen Vorkommen der Phyla zwischen Sättigungstypen
Firmicutes
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Firmicutes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Firmicutes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Firmicutes)) + xlab('Time') + ylab('log10 (Relative Abundance p__Firmicutes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Actinobacteria
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Actinobacteria, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Actinobacteria, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Actinobacteria)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Actinobacteria)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Actinobacteria)) + xlab('Time') + ylab('log10 (Relative Abundance p__Actinobacteria)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
phylum_ST$k__Bacteria.p__Actinobacteria
Bacteroidetes In Arbeit
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)
library(scales)
pdf("/Users/student05/Documents/fertige Plots/sat.types.bacteroidetes.neu.pdf",width=8, height=10)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes, fill= Phenotype)) +
xlab('Phenotype') + ylab('Relatives Vorkommen p__Bacteroidetes [%]') +
geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=45, hjust=1))+
scale_y_log10(labels = percent_format())
dev.off()
ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Bacteroidetes)) + xlab('Time') + ylab('log10 (Relative Abundance p__Bacteroidetes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Proteobacteria
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Proteobacteria, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Proteobacteria, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Proteobacteria)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Proteobacteria)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Proteobacteria)) + xlab('Time') + ylab('log10 (Relative Abundance p__Proteobacteria)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Tenericutes
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Tenericutes, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Tenericutes, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Tenericutes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Tenericutes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Tenericutes)) + xlab('Time') + ylab('log10 (Relative Abundance p__Tenericutes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Verrucomicrobia In Arbeit
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Verrucomicrobia)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Verrucomicrobia)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
scale_y_log10(labels = percent_format())
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Bacteroidetes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Bacteroidetes)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1)) +
scale_y_log10(labels = percent_format())
ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Verrucomicrobia)) + xlab('Time') + ylab('log10 (Relative Abundance p__Verrucomicrobia)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
pdf("/Users/student05/Documents/fertige Plots/sat.types.verrucomicrobia.neu.pdf",width=8, height=10)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Verrucomicrobia, fill= Phenotype)) +
xlab('Phenotype') + ylab('Relatives Vorkommen p__Verrucomicrobia [%]') +
geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = )+
theme(legend.position="none")+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=45, hjust=1))+
scale_y_log10(labels = percent_format())
dev.off()
Laden der Genus-Metadaten s.o. Synchronisieren der Daten, hinzufügen von log-Transformation und Pseudocount 0.00001
common.ids.relab <- intersect(rownames(FA_stool.ST), rownames(relab_genus_ID))
FA_stool.ST <- FA_stool.ST[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
relab_genus_ID1 <- relab_genus_ID[,c(3:31)] + 0.00001
relab_genus_ID_log <- log10(relab_genus_ID_log)
genus_ST <- cbind(relab_genus_ID1, FA_stool.ST)
Wilcoxon-Test zur Bestimmung von Unterschiede im relativen Vorkommen der Gattungen zwischen Sättigungstypen
Bifidobacterium
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Bifidobacterium)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium)) + xlab('Time') + ylab('log10 (Relative Abundance g__Bifidobacterium)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Faecalibacterium
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Faecalibacterium)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
scale_y_log10(labels = percent_format())
ggplot(subset(filter(genus_ST)), aes(x=Time,y=10^k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium)) + xlab('Time') + ylab('log10 (Relative Abundance g__Faecalibacterium)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)+
scale_y_log10(labels = percent_format())
Sutterella
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Sutterella)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella)) + xlab('Time') + ylab('log10 (Relative Abundance g__Sutterella)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Oscillospira In Arbeit
mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(genus_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(genus_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Oscillospira)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
scale_y_log10(labels = percent_format())
pdf("/Users/student05/Documents/fertige Plots/sat.types.oscillo.neu.pdf",width=8, height=10)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, fill= Phenotype)) +
xlab('Phänotyp') + ylab('Relatives Vorkommen g__Oscillospira [%]') +
geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=45, hjust=1))+
scale_y_log10(labels = percent_format())
dev.off()
ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)) + xlab('Time') + ylab('log10 (Relative Abundance g__Oscillospira)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Akkermansia In Arbeit
mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(genus_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(genus_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Akkermansia)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
scale_y_log10(labels = percent_format())
pdf("/Users/student05/Documents/fertige Plots/sat.types.akkermansia.neu.pdf",width=8, height=10)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, fill= Phenotype)) +
xlab('Phänotyp') + ylab('Relatives Vorkommen g__Akkermansia [%]') +
geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = )+
theme(legend.position="none")+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=45, hjust=1))+
scale_y_log10(labels = percent_format())
dev.off()
ggplot(subset(filter(genus_ST)), aes(x=Time,y=10^k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)) + xlab('Time') + ylab('log10 (Relative Abundance g__Akkermansia)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)+
scale_y_log10(labels = percent_format())
Bacteroides
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Bacteroides)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides)) + xlab('Time') + ylab('log10 (Relative Abundance g__Bacteroides)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Prevotella
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Prevotella)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella)) + xlab('Time') + ylab('log10 (Relative Abundance g__Prevotella)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Dorea
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Dorea)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea)) + xlab('Time') + ylab('log10 (Relative Abundance g__Dorea)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Collinsella
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Collinsella)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella)) + xlab('Time') + ylab('log10 (Relative Abundance g__Collinsella)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Rikenellaceae
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__)) + xlab('Phenotype') + ylab('log10 (Relative Abundance f__Rikenellaceae)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)
ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__)) + xlab('Time') + ylab('log10 (Relative Abundance f__Rikenellaceae)') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
FA_stool.PRE <- subset(filter(FA_stool.ST, Time == "PRE"))
FA_stool.POST <- subset(filter(FA_stool.ST, Time == "POST"))
Unterschiede im Firmicutes/Bacteroidetes-ratio zwischen den Sättigungstypen
Laden der Metadaten, Bestimmen von Mean und SD
phylum_ST <- read.table("/Users/student05/Documents/fa feces/FA fecal/saturation types/phylum.phenotype.txt", sep ='\t', comment='', head=T)
phylum_ST$Time <-factor(phylum_ST$Time, levels = c("PRE", "POST"))
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$F_B_ratio)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$F_B_ratio)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$F_B_ratio)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$F_B_ratio)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$F_B_ratio)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$F_B_ratio)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$F_B_ratio)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$F_B_ratio)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$F_B_ratio)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$F_B_ratio)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$F_B_ratio)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$F_B_ratio)
mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$F_B_ratio)
sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$F_B_ratio)
mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$F_B_ratio)
sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$F_B_ratio)
Wilcoxon-Test zur Bestimmung von Unterschiede im F/B-ratio zwischen Sättigungstypen
In Arbeit
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$F_B_ratio, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$F_B_ratio, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.sat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.unsat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=F_B_ratio)) + xlab('Phenotype') + ylab('Firmicutes/Bacteroidetes ratio') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
pdf("/Users/student05/Documents/fertige Plots/sat.types.F.B.pdf",width=8, height=10)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=F_B_ratio, fill= Phenotype)) +
xlab('Phenotype') + ylab('Firmicutes/Bacteroidetes Verhältnis') +
geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
theme(legend.position="none")+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=45, hjust=1))
dev.off()
types <- melt(phylum_ST, id.vars = c('Time', 'F_B_ratio'), measure.vars = c('sat', 'unsat'))
types.pr <- subset(filter(types, !Time == 'POST'))
types.po <- subset(filter(types, !Time == 'PRE'))
types <-dplyr::rename(types, FA=variable)
types <- dplyr::rename(types, Concentration=value)
pairwise.wilcox.test(subset(filter(convT, Time == "PRE"))$F_B_ratio, subset(filter(convT, Time == "PRE"))$Phenotype2, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT, Time == "POST"))$F_B_ratio, subset(filter(convT, Time == "POST"))$Phenotype2, p.adjust.method = 'BH', paired = F)
ggscatter(types.pr, x='Concentration', y='F_B_ratio',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ FA)+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
phylum_ST$Time <- factor(phylum_ST$Time, levels = c("PRE", "POST"))
pdf("/Users/student05/Documents/fertige Plots/sat.bact.firm.pdf",width=8, height=10)
ggscatter(phylum_ST, x='sat', y='F_B_ratio',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', size=2.5,cor.coef.coord =c(250, 19),cor.coef.size = 7,conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Firmicutes/Bacteroidetes Verhältnis')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
dev.off()
pdf("/Users/student05/Documents/fertige Plots/unsat.bact.firm.pdf",width=8, height=10)
ggscatter(phylum_ST, x='unsat', y='F_B_ratio',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line',size=2.5, cor.coef.coord =c(250, 19),cor.coef.size = 7,conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Firmicutes/Bacteroidetes Verhältnis')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
dev.off()
ggscatter(types.po, x='Concentration', y='F_B_ratio',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, 6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ FA)+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(types, x='Concentration', y='F_B_ratio',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, 16),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ FA)+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
- Analysen mit dem Firmicutes/Bacteroidetes-ratio Unterschiede im F/B-ratio zwischen Sterolkonvertierungstypen Unterteilen in high und low converter
lowconv <- filter(phylum_ST, Proband == "05AP" | Proband == "33MP"
| Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
| Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")
lowconv['Phenotype2'] = 'low converter'
highconv <- filter(phylum_ST, Proband == "06WT" | Proband == "07RW"
| Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
| Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
| Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
| Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
highconv['Phenotype2'] = 'high converter'
highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL
noconv <- filter(phylum_ST, Proband == "28HM" | Proband == "32FG"
| Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
| Proband == "39DA" | Proband == "66DG" | Proband == "70PL")
noconv['Phenotype2'] = 'not classified'
noconv$Converter.Type <- NULL
convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)
comparison_conv <- list(c("low converter", "high converter"))
convT <- subset(filter(convT, !Phenotype2 == 'not classified'))
pairwise.wilcox.test(subset(filter(convT, Time == "PRE"))$F_B_ratio, subset(filter(convT, Time == "PRE"))$Phenotype2, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT, Time == "POST"))$F_B_ratio, subset(filter(convT, Time == "POST"))$Phenotype2, p.adjust.method = 'BH', paired = F)
ggplot(subset(filter(convT)), aes(x=Phenotype2,y=F_B_ratio)) + xlab('Phenotype') + ylab('Firmicutes/Bacteroidetes ratio') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Time) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_conv)+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
ggplot(subset(filter(convT)), aes(x=Time,y=F_B_ratio)) + xlab('Time') + ylab('Firmicutes/Bacteroidetes ratio') +
geom_boxplot(fill = 'whitesmoke', color="black") +
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
facet_wrap(~Phenotype2) +
stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
Korrelationsanalysen zwischen allen fäkalen Fettsäuren und dem F/B-ratio Loop und Plots zu gesättigte Fettsäuren und F/B-ratio In Arbeit
phylum_colnames <- colnames(phylum_ST[, c(3:9)])
corr_map_phylum_sat <- filter(phylum_ST, !is.na(sat))
corr_spearman_Phylum_sat <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_sat, !is.na(i))
y = tmp[,i]
x = tmp$sat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$sat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$sat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_sat)+1
corr_spearman_Phylum_sat[nrow,"FA"] <- "sat"
corr_spearman_Phylum_sat[nrow, "Phylum"] = i
corr_spearman_Phylum_sat[nrow, "p.value"] = p
corr_spearman_Phylum_sat[nrow, "rho"] = rho
corr_spearman_Phylum_sat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_sat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_sat[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_sat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_sat$p.adjusted <- p.adjust(corr_spearman_Phylum_sat$p.value, method = "BH", n = 35)
corr_spearman_Phylum_sat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_sat$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_sat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_sat$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_sat <- filter(corr_spearman_Phylum_sat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_sat, file = '/Users/student05/Documents/FB q-value/sat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_ST, x='sat', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'saturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='sat', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'saturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
pdf("/Users/student05/Documents/fertige Plots/sat.bact.firm.pdf",width=8, height=10)
ggscatter(phylum_ST, x='sat', y='F_B_ratio',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', cor.coef.coord =c(250, 19),cor.coef.size = 7,conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Firmicutes/Bacteroidetes Verhältnis')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
dev.off()
Loop und Plots zu ungesättigte Fettsäuren und F/B-ratio
phylum_colnames <- colnames(phylum_ST[, c(3:9)])
corr_map_phylum_unsat <- filter(phylum_ST, !is.na(unsat))
corr_spearman_Phylum_unsat <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_unsat, !is.na(i))
y = tmp[,i]
x = tmp$unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_unsat)+1
corr_spearman_Phylum_unsat[nrow,"FA"] <- "unsat"
corr_spearman_Phylum_unsat[nrow, "Phylum"] = i
corr_spearman_Phylum_unsat[nrow, "p.value"] = p
corr_spearman_Phylum_unsat[nrow, "rho"] = rho
corr_spearman_Phylum_unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_unsat$p.adjusted <- p.adjust(corr_spearman_Phylum_unsat$p.value, method = "BH", n = 35)
corr_spearman_Phylum_unsat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_unsat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_unsat$p.value_POST, method = "BH", n = 35)
corr_sig_Phylum_unsat <- filter(corr_spearman_Phylum_unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)
write.table(corr_spearman_Phylum_unsat, file = '/Users/student05/Documents/FB q-value/unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plots zu einfach ungesättigten FA und F/B-ratio
ggscatter(phylum_ST, x='mono.unsat', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Monounsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='mono.unsat', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Monounsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
Plots zu zweifach ungesättigten FA - total FA und F/B-ratio
ggscatter(phylum_ST, x='di.unsat', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Diunsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='di.unsat', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Diunsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='more.2.unsat', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='more.2.unsat', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='less.14', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '< 14 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='more.2.unsat', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='c14.17', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= ' 14-17 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='c14.17', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '> 14-17 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='c18.19', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= ' 18-19 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='c18.19', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '18-19 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='c20.21', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= ' 20-21 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='c20.21', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '20-21 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='c22.24', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= ' 22-24 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='c20.21', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= '20-21 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='total', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= ' total fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_ST, x='total', y='F_B_ratio', add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'total fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
5.2 Korrelationen gesättigte FA und ungesättigte FA mit phylum-level
Filtern der Phylum-Metadaten, hinzufügen von log-Transformation und Pseudocount 0.0001
phylum_ST_log <- phylum_ST[,c(3:8)] + 0.00001
phylum_ST_log <- log10(phylum_ST_log)
phylum_ST <- cbind(phylum_ST_log, phylum_ST[, c(9:33)])
phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])
corr_map_phylum_unsat <- filter(phylum_ST, !is.na(unsat))
corr_spearman_Phylum_unsat <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_unsat, !is.na(i))
y = tmp[,i]
x = tmp$unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time.1 == "PRE"))[,i]
w = subset(filter(tmp, Time.1 == "PRE"))$unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time.1 == "POST"))[,i]
s = subset(filter(tmp, Time.1 == "POST"))$unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_unsat)+1
corr_spearman_Phylum_unsat[nrow,"FA"] <- "unsat"
corr_spearman_Phylum_unsat[nrow, "Phylum"] = i
corr_spearman_Phylum_unsat[nrow, "p.value"] = p
corr_spearman_Phylum_unsat[nrow, "rho"] = rho
corr_spearman_Phylum_unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_unsat$p.adjusted <- p.adjust(corr_spearman_Phylum_unsat$p.value, method = "BH", n = 35)
corr_spearman_Phylum_unsat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_unsat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_unsat$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_unsat, file = '/Users/student05/Documents/unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten der Korrelationen zwischen gesättigten und ungesättigten FA und phylum-level
Teilweise in Arbeit
phylum_ST$k__Bacteria.p__Firmicutes
melt.Fi <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Firmicutes'), measure.vars = c('sat', 'unsat'))
melt.Fi<-dplyr::rename(melt.Fi, FA=variable)
melt.Fi <- dplyr::rename(melt.Fi, Concentration=value)
melt.Fi.pr <- subset(filter(melt.Fi, !Time.1 == 'POST'))
melt.Fi.po <- subset(filter(melt.Fi, !Time.1 == 'PRE'))
melt.Fi.pr$k__Bacteria.p__Firmicutes
ggscatter(melt.Fi.pr, x='Concentration', y='k__Bacteria.p__Firmicutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -0.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Firmicutes')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Fi.po, x='Concentration', y='k__Bacteria.p__Firmicutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.75),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Firmicutes')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Fi, x='Concentration', y='k__Bacteria.p__Firmicutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.7),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Firmicutes')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
phylum_ST$k__Bacteria.p__Bacteroidetes
melt.Ba <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Bacteroidetes'), measure.vars = c('sat', 'unsat'))
melt.Ba<-dplyr::rename(melt.Ba, FA=variable)
melt.Ba <- dplyr::rename(melt.Ba, Concentration=value)
melt.Ba.pr <- subset(filter(melt.Ba, !Time.1 == 'POST'))
melt.Ba.po <- subset(filter(melt.Ba, !Time.1 == 'PRE'))
ggscatter(melt.Ba.pr, x='Concentration', y='k__Bacteria.p__Bacteroidetes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -0.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Bacteroidetes')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ba.po, x='Concentration', y='k__Bacteria.p__Bacteroidetes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.95),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Bacteroidetes')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ba, x='Concentration', y='k__Bacteria.p__Bacteroidetes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.9),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Bacteroidetes')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
phylum_ST$Time.1 <- factor(phylum_ST$Time.1, levels = c("PRE", "POST"))
pdf("/Users/student05/Documents/fertige Plots/unsat.bacteroidetes.pdf",width=8, height=10)
ggscatter(phylum_ST, x='unsat', y='k__Bacteria.p__Bacteroidetes',color = 'Time.1', palette = c('skyblue', 'orchid'), add = 'reg.line', cor.coef.coord =c(0, -0.8),cor.coef.size = 7,conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'log10 (Relatives Vorkommen p__Bacteroidetes)')+
facet_grid(.~ Time.1, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=45, hjust=1))+
theme(legend.position="none")
dev.off()
phylum_ST$k__Bacteria.p__Verrucomicrobia
melt.Ve <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Verrucomicrobia'), measure.vars = c('sat', 'unsat'))
melt.Ve<-dplyr::rename(melt.Ve, FA=variable)
melt.Ve <- dplyr::rename(melt.Ve, Concentration=value)
melt.Ve.pr <- subset(filter(melt.Ve, !Time.1 == 'POST'))
melt.Ve.po <- subset(filter(melt.Ve, !Time.1 == 'PRE'))
ggscatter(melt.Ve.pr, x='Concentration', y='k__Bacteria.p__Verrucomicrobia',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -0.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Verrucomicrobia')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ve.po, x='Concentration', y='k__Bacteria.p__Verrucomicrobia',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.3),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Verrucomicrobia')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ve, x='Concentration', y='k__Bacteria.p__Verrucomicrobia',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Verrucomicrobia')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
phylum_ST$k__Bacteria.p__Tenericutes
melt.Te <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Tenericutes'), measure.vars = c('sat', 'unsat'))
melt.Te<-dplyr::rename(melt.Te, FA=variable)
melt.Te <- dplyr::rename(melt.Te, Concentration=value)
melt.Te.pr <- subset(filter(melt.Te, !Time.1 == 'POST'))
melt.Te.po <- subset(filter(melt.Te, !Time.1 == 'PRE'))
ggscatter(melt.Te.pr, x='Concentration', y='k__Bacteria.p__Tenericutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(350, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Tenericutes')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Te.po, x='Concentration', y='k__Bacteria.p__Tenericutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(600, -1.3),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Tenericutes')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Te, x='Concentration', y='k__Bacteria.p__Tenericutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Tenericutes')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
phylum_ST$k__Bacteria.p__Actinobacteria
melt.Ac <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Actinobacteria'), measure.vars = c('sat', 'unsat'))
melt.Ac<-dplyr::rename(melt.Ac, FA=variable)
melt.Ac <- dplyr::rename(melt.Ac, Concentration=value)
t
melt.Ac.pr <- subset(filter(melt.Ac, !Time.1 == 'POST'))
melt.Ac.po <- subset(filter(melt.Ac, !Time.1 == 'PRE'))
ggscatter(melt.Ac.pr, x='Concentration', y='k__Bacteria.p__Actinobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Actinobacteria')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ac.po, x='Concentration', y='k__Bacteria.p__Actinobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -1.4),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Actinobacteria')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ac, x='Concentration', y='k__Bacteria.p__Actinobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.35),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Actinobacteria')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
phylum_ST$k__Bacteria.p__Proteobacteria
melt.Pr <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Proteobacteria'), measure.vars = c('sat', 'unsat'))
melt.Pr<-dplyr::rename(melt.Pr, FA=variable)
melt.Pr <- dplyr::rename(melt.Pr, Concentration=value)
melt.Pr.pr <- subset(filter(melt.Pr, !Time.1 == 'POST'))
melt.Pr.po <- subset(filter(melt.Pr, !Time.1 == 'PRE'))
ggscatter(melt.Pr.pr, x='Concentration', y='k__Bacteria.p__Proteobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.6),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Proteobacteria')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Pr.po, x='Concentration', y='k__Bacteria.p__Proteobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -1.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Proteobacteria')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Pr, x='Concentration', y='k__Bacteria.p__Proteobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.35),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Proteobacteria')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
5.3 Korrelationen gesättigte FA und ungesättigte FA mit genus-level
Filtern der Genus-Metadaten, hinzufügen von log-Transformation und Pseudocount 0.0001 Loop Genus-level
FA_stool.ST <- mutate(FA_stool.ST, SampleID1 = paste(Proband, Time, sep = "."))
row.names(FA_stool.ST) <- FA_stool.ST$SampleID1
genus_colnames <- colnames(relab_genus_spread[, c(3:31)])
common.ids.relab <- intersect(rownames(FA_stool.ST), rownames(relab_phylum_ID))
FA_stool.ST <- FA_stool.ST[common.ids.relab,]
relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
relab_genus_ID_log <- relab_genus_ID[,c(3:31)] + 0.00001
relab_genus_ID_log <- log10(relab_genus_ID_log)
genus_FA <- cbind(relab_genus_ID_log, FA_stool.ST)
corr_map_genus_unsat <- filter(genus_FA, !is.na(unsat))
corr_spearman_genus_unsat <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_unsat, !is.na(i))
y = tmp[,i]
x = tmp$unsat
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$unsat
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$unsat
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_unsat)+1
corr_spearman_genus_unsat[nrow,"FA"] = "unsaturated"
corr_spearman_genus_unsat[nrow, "Genus"] = i
corr_spearman_genus_unsat[nrow, "p.value"] = p
corr_spearman_genus_unsat[nrow, "rho"] = rho
corr_spearman_genus_unsat[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_unsat[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_unsat[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_unsat[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_unsat$p.adjusted <- p.adjust(corr_spearman_genus_unsat$p.value, method = "BH", n = 35)
corr_spearman_genus_unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_unsat$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_unsat$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_genus_unsat, file = '/Users/student05/Documents/unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten der Korrelationen zwischen gesättigten und ungesättigten FA und genus-level
Teilweise in Arbeit
genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira
pdf("/Users/student05/Documents/fertige Plots/unsat.oscillo.pdf",width=8, height=10)
ggscatter(genus_FA, x='unsat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', cor.coef.coord =c(0, -2),cor.coef.size = 7,conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'log10 (Relatives Vorkommen g__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
dev.off
melt.Os <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira'), measure.vars = c('sat', 'unsat'))
melt.Os<-dplyr::rename(melt.Os, FA=variable)
melt.Os <- dplyr::rename(melt.Os, Concentration=value)
melt.Os.pr <- subset(filter(melt.Os, !Time == 'POST'))
melt.Os.po <- subset(filter(melt.Os, !Time == 'PRE'))
ggscatter(melt.Os.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -2),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Oscillospira')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Os.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -2),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Oscillospira')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Os, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -2),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Oscillospira')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium
melt.Bi <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium'), measure.vars = c('sat', 'unsat'))
melt.Bi<-dplyr::rename(melt.Bi, FA=variable)
melt.Bi <- dplyr::rename(melt.Bi, Concentration=value)
melt.Bi.pr <- subset(filter(melt.Bi, !Time == 'POST'))
melt.Bi.po <- subset(filter(melt.Bi, !Time == 'PRE'))
ggscatter(melt.Bi.pr, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bifidobacterium')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Bi.po, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bifidobacterium')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Bi, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.3),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bifidobacterium')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella
melt.Co <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella'), measure.vars = c('sat', 'unsat'))
melt.Co<-dplyr::rename(melt.Co, FA=variable)
melt.Co <- dplyr::rename(melt.Co, Concentration=value)
melt.Co.pr <- subset(filter(melt.Co, !Time == 'POST'))
melt.Co.po <- subset(filter(melt.Co, !Time == 'PRE'))
ggscatter(melt.Co.pr, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Collinsella')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Co.po, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Collinsella')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Co, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Collinsella')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium
melt.Fa <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium'), measure.vars = c('sat', 'unsat'))
melt.Fa<-dplyr::rename(melt.Fa, FA=variable)
melt.Fa <- dplyr::rename(melt.Fa, Concentration=value)
melt.Fa.pr <- subset(filter(melt.Fa, !Time == 'POST'))
melt.Fa.po <- subset(filter(melt.Fa, !Time == 'PRE'))
ggscatter(melt.Fa.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Faecalibacterium')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Fa.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Faecalibacterium')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Fa, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Faecalibacterium')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia
melt.Ak <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia'), measure.vars = c('sat', 'unsat'))
melt.Ak<-dplyr::rename(melt.Ak, FA=variable)
melt.Ak <- dplyr::rename(melt.Ak, Concentration=value)
melt.Ak.pr <- subset(filter(melt.Ak, !Time == 'POST'))
melt.Ak.po <- subset(filter(melt.Ak, !Time == 'PRE'))
ggscatter(melt.Ak.pr, x='Concentration', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Akkermansia')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ak.po, x='Concentration', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Akkermansia')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ak, x='Concentration', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Akkermansia')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia
melt.Bl <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia'), measure.vars = c('sat', 'unsat'))
melt.Bl<-dplyr::rename(melt.Bl, FA=variable)
melt.Bl <- dplyr::rename(melt.Bl, Concentration=value)
melt.Bl.pr <- subset(filter(melt.Bl, !Time == 'POST'))
melt.Bl.po <- subset(filter(melt.Bl, !Time == 'PRE'))
ggscatter(melt.Bl.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Blautia')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Bl.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Blautia')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Bl, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Blautia')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides
melt.Bc <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides'), measure.vars = c('sat', 'unsat'))
melt.Bc<-dplyr::rename(melt.Bc, FA=variable)
melt.Bc <- dplyr::rename(melt.Bc, Concentration=value)
melt.Bc.pr <- subset(filter(melt.Bc, !Time == 'POST'))
melt.Bc.po <- subset(filter(melt.Bc, !Time == 'PRE'))
ggscatter(melt.Bc.pr, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(200, -0.8),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bacteroides')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Bc.po, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.2),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bacteroides')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Bc, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bacteroides')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus
melt.Cp <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus'), measure.vars = c('sat', 'unsat'))
melt.Cp<-dplyr::rename(melt.Cp, FA=variable)
melt.Cp <- dplyr::rename(melt.Cp, Concentration=value)
melt.Cp.pr <- subset(filter(melt.Cp, !Time == 'POST'))
melt.Cp.po <- subset(filter(melt.Cp, !Time == 'PRE'))
ggscatter(melt.Cp.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(200, -1.6),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Coprococcus')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Cp.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Coprococcus')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Cp, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Coprococcus')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.
melt.Ru <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.'), measure.vars = c('sat', 'unsat'))
melt.Ru<-dplyr::rename(melt.Ru, FA=variable)
melt.Ru <- dplyr::rename(melt.Ru, Concentration=value)
melt.Ru.pr <- subset(filter(melt.Ru, !Time == 'POST'))
melt.Ru.po <- subset(filter(melt.Ru, !Time == 'PRE'))
ggscatter(melt.Ru.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(200, -2),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Ruminococcus')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ru.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -2),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Ruminococcus')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Ru, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.9),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Ruminococcus')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
genus_FA$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella
melt.Pe <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella'), measure.vars = c('sat', 'unsat'))
melt.Pe<-dplyr::rename(melt.Pe, FA=variable)
melt.Pe <- dplyr::rename(melt.Pe, Concentration=value)
melt.Pe.pr <- subset(filter(melt.Pe, !Time == 'POST'))
melt.Pe.po <- subset(filter(melt.Pe, !Time == 'PRE'))
ggscatter(melt.Pe.pr, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(200, -0.6),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Prevotella')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Pe.po, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Prevotella')+
facet_grid(.~ FA, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('POST')+
theme(plot.title = element_text(color="black", size=14))
ggscatter(melt.Pe, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'FA', palette = c('steelblue2', 'deeppink2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Prevotella')+
facet_grid(.~ FA,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")+
ggtitle('Times together')+
theme(plot.title = element_text(color="black", size=14))
- Serumlipidanalyse
Laden der Daten und testen auf Normalverteilung
LI_serum <- read.table("/Users/student05/Documents/serum lipids zahlen.1-2.txt", sep = '\t', comment='',head=T)
LI_serum$Time <-factor(LI_serum$Time, levels = c("PRE", "POST"))
row.names(LI_serum) <- LI_serum$SampleID
nd.LI<- data_frame()
for (i in LI_colnames) {
fit <- shapiro.test(as.matrix(as.data.frame(lapply(LI_serum[,i],as.numeric))))
p = fit$p.value
nrow = nrow(nd.LI)+1
nd.LI[nrow, "column"] = i
nd.LI[nrow, "p.value"] = round(p, 4)
}
ggqqplot(LI_serum$Sum, ylab = "Sum serum lipids [nmol/ml]", xlab = "SampleID")
ggqqplot(LI_serum$PE, ylab = "Phosphatidylethanolamine
serum lipids [nmol/ml]", xlab = "SampleID")
Alle Probanden zeigen PRE und POST Proben, Follow-up nicht vorhanden
Loop für Wilcoxon-Test zwischend den Zeitpunkten PRE und POST
wilcox_LI<- data_frame()
for (i in LI_colnames) {
tmp <- LI_serum %>% drop_na(i)
x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y <- LI_serum$Time
tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = T)
p <- tmp_wilcox$p.value
nrow = nrow(wilcox_LI)+1
wilcox_LI[nrow, "LI"] <- i
wilcox_LI[nrow, "Mean PRE"] <-round(mean(subset(filter(LI_serum,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
wilcox_LI[nrow, "sd PRE"] <-round(sd(c(subset(filter(LI_serum,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), na.rm = TRUE)), 4)
wilcox_LI[nrow, "Mean POST"] <-round(mean(subset(filter(LI_serum,Time == "POST")[,i],!is.na(i), na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
wilcox_LI[nrow, "sd POST"] <- round(sd(c(subset(filter(LI_serum,Time == "POST")[,i],!is.na(i), na.rm = TRUE),na.rm = TRUE)), 4)
wilcox_LI[nrow, "p.value"] <- round(p, 4) }
write.table(wilcox_LI, file = '/Users/student05/Documents/serum lipids/Tabellen/LI.pre.post.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten der Serumlipide zu den Zeitpunkten PRE und POST In Arbeit, unterteilt in Gylcerophospholipide und Sphingolipide
LI_serum.melt2 <- melt(LI_serum, id.vars = 'Time', measure.vars = c('PC', 'PCO', 'PE', 'PI','PEP', 'LPC'))
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, LI=variable)
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/SL.KD.pdf",width=9, height=10)
ggplot(LI_serum.melt2,aes(x=Time, y=Concentration, fill= LI)) +
xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/ml]') +
geom_boxplot(width = .7, lwd=0.6)+ theme_classic()+
scale_fill_manual(labels = c("Phosphatidylcholine", "PCO", "Phosphatidylethanolamine", "Phosphatidylinositol", "PE based plasmalogens", "Lysophosphatidylcholine"),
values = c("#980043", "#dd1c77", "#df65b0", "#c994c7", "#d4b9da", "#f1eef6")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c( "POST")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),axis.text=element_text(size=16))+
theme(legend.position="top")
dev.off()
LI_serum.melt1 <- melt(LI_serum, id.vars = 'Time', measure.vars = c('SM','CER'))
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, LI=variable)
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/SL2.KD.pdf",width=8, height=10)
ggplot(LI_serum.melt1,aes(x=Time, y=Concentration, fill= LI)) +
xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/ml]') +
geom_boxplot(width = .3, lwd=0.8)+ theme_classic()+
scale_fill_manual(labels = c("Sphingomyelin", "Ceramide"),
values = c("#fa9fb5", "#fde0dd")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c( "POST")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),axis.text=element_text(size=16))+
theme(legend.position="top")+
expand_limits(y=c(0, 2300))
dev.off()
Alle Serumlipide zusammen, Prozentuale Verteilung
LI_serum.melt <- melt(LI_serum, id.vars = 'Time', measure.vars = c('PC', 'PCO','SM', 'PE', 'PI','PEP', 'LPC','CER'))
LI_serum.melt <- dplyr::rename(LI_serum.melt, LI=variable)
LI_serum.melt <- dplyr::rename(LI_serum.melt, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/Serumlipids.alltimes.pdf",width=9, height=10)
ggplot(LI_serum.melt,aes(x=LI, y=Concentration, fill= LI)) +
xlab ('Serumlipid') + ylab ('Konzentration [nmol/ml]') +
scale_x_discrete(labels=c("PC" = "Phosphatidylcholine", "PCO" = "Phosphatidylcholine Plasmogen", "SM" = "Sphingomyelin", "PE" = "Phosphatidylethanolamine", "PI" = "Phosphatidylinositol", "PEP" = "Phosphatidylethanolamin Plasmalogen", "LPC" = "Lysophosphatidylcholine", "CER" ="Ceramid"))+
geom_boxplot(width = .5, lwd=0.5) + theme_classic()+
scale_fill_manual(labels = c("Phosphatidylcholine", "Phosphatidylcholine plasmogen", "Sphingomyelin", "Phosphatidylethanolamine", "Phosphatidylinositol", "PE based plasmalogens", "Lysophosphatidylcholine", "Ceramide"),
values = c("#df65b0", "#df65b0", "#fa9fb5", "#df65b0", "#df65b0", "#df65b0", "#df65b0", "#fa9fb5")) +
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(angle=25, hjust=1))+
theme(legend.position="top")
dev.off()
LI_serum.sum.melt <- melt(LI_serum, id.vars = 'Time', measure.vars = c('Sum', 'Sum.Membrane','Sum.Storage', 'Sum.Lyso'))
LI_serum.sum.melt <- rename(LI_serum.sum.melt, LI=variable)
LI_serum.sum.melt <- rename(LI_serum.sum.melt, Concentration=value)
ggplot(LI_serum.sum.melt,aes(x=Time, y=Concentration, fill= LI)) +
xlab ('Time Point') + ylab ('Concentration [nmol/ml]') +
geom_boxplot() +
scale_fill_manual(labels = c("Summary total", "Summary Membrane", "Summary Storage", "Summary Lyso"),
values = c("tomato", "yellowgreen", "steelblue2", "orchid2")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI_serum.p.melt <- melt(LI_serum, id.vars = 'Time', measure.vars = c( 'P.Membrane','P.Storage', 'P.Lyso'))
LI_serum.p.melt <- rename(LI_serum.p.melt, LI=variable)
LI_serum.p.melt <- rename(LI_serum.p.melt, Concentration=value)
ggplot(LI_serum.p.melt,aes(x=Time, y=Concentration, fill= LI)) +
xlab ('Time Point') + ylab ('Concentration [nmol/ml]') +
geom_boxplot() +
scale_fill_manual(labels = c( "Percentage Membrane", "Percentage Storage", "Percentage Lyso"),
values = c("yellowgreen", "steelblue2", "orchid2")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
Plotten der einzelnen Serumlipide, linked by Proband
ggpaired(LI_serum, x='Time', y='Sum', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Timepoint') + ylab('Concentration Summary serum lipids [nmol/ml]') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='PC', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('PC serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='PCO', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('PCO serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='SM', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('SM serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='LPC', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Timepoint') + ylab('LPC serum lipids Concentration [nmol/ml]') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='PI', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('PI serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='PEP', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('PEP serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='LPC', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('LPC serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='CER', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Timepoint') + ylab('CER serum lipids Concentration[nmol/ml]') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='CER', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('CER serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")+
geom_point(aes(color=Time), alpha=0.5) +
geom_boxplot(outlier.size=4, outlier.colour='blue', alpha=0.5)
ggpaired(LI_serum, x='Time', y='HexCer', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Timepoint') + ylab('HexCer serum lipids Concentration [nmol/ml]') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='Sum.Membrane', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Membrane serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='Sum.Storage', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Storage serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='Sum.Lyso', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Lyso serum lipids [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='P.Membrane', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Membrane serum lipids [%]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='LPC.PC', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('LPC/PC serum lipid ratio') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='CER.SM', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Cer/SM serum lipid ratio') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='HexCer.CER', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('HexCer/Cer serum lipid ratio') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
ggpaired(LI_serum, x='Time', y='PC.PE', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('PC/PE serum lipid ratio') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
Korrelationen zwischen einzelnen Serumlipiden, linked by Probands
LI_serum.melt1 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('PC', 'LPC'))
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, LI=variable)
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, Concentration=value)
LI_serum.melt1$Time <- factor(LI_serum.melt1$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt1, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)
LI_serum.melt2 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('Sum.Storage', 'Sum.Membrane'))
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, LI=variable)
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, Concentration=value)
LI_serum.melt2$Time <- factor(LI_serum.melt2$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt2, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)
LI_serum.melt2 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('Sum.Membrane', 'Sum.Lyso'))
LI_serum.melt2 <- rename(LI_serum.melt2, LI=variable)
LI_serum.melt2 <- rename(LI_serum.melt2, Concentration=value)
LI_serum.melt2$Time <- factor(LI_serum.melt2$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt2, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)
LI_serum.melt3 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('CER.SM', 'HexCer.CER'))
LI_serum.melt3<- rename(LI_serum.melt3, LI=variable)
LI_serum.melt3 <- rename(LI_serum.melt3, Concentration=value)
LI_serum.melt3$Time <- factor(LI_serum.melt2$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt3, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")
LI_serum.melt4 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('CER.SM', 'LPC'))
LI_serum.melt4<- rename(LI_serum.melt4, LI=variable)
LI_serum.melt4 <- rename(LI_serum.melt4, Concentration=value)
LI_serum.melt4$Time <- factor(LI_serum.melt4$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt4, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")
LI_serum.melt5 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('PC.PE'))
LI_serum.melt5<- dplyr::rename(LI_serum.melt5, LI=variable)
LI_serum.melt5 <- dplyr::rename(LI_serum.melt5, Concentration=value)
LI_serum.melt5$Time <- factor(LI_serum.melt5$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt5, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'LI', short.panel.labs = FALSE) +
xlab('Timepoint') + ylab('PC/PE ratio') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ LI, scales="free")+
theme(legend.position="none")
LI_serum.melt6 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('P.Storage', 'HexCer'))
LI_serum.melt6<- rename(LI_serum.melt6, LI=variable)
LI_serum.melt6 <- rename(LI_serum.melt6, Concentration=value)
LI_serum.melt6$Time <- factor(LI_serum.melt6$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt6, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")+
theme(legend.position="none")
LI_serum.melt6 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('LPC', 'PE'))
LI_serum.melt6<- rename(LI_serum.melt6, LI=variable)
LI_serum.melt6 <- rename(LI_serum.melt6, Concentration=value)
LI_serum.melt6$Time <- factor(LI_serum.melt6$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt6, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")+
theme(legend.position="none")
LI_serum.1 <- read.table("/Users/student05/Documents/serum lipids/serum lipids zahlen.1.2.txt", sep = '\t', comment='',head=T)
LI_serum.melt6 <- melt(LI_serum.1, id.vars = c('Proband'), measure.vars = c('Sum', 'PE'))
LI_serum.melt6<- rename(LI_serum.melt6, LI=variable)
LI_serum.melt6 <- rename(LI_serum.melt6, Concentration=value)
LI_serum.melt6$Time <- factor(LI_serum.melt6$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt6, x='LI', y='Concentration', color = 'black', fill = 'LI', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")
Korrelationen zwischen den Serumlipiden, von welchen auch die Verhältnisse betrachtet wurden
LI_serum.melt6 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('LPC', 'PE'))
LI_serum.melt6<- rename(LI_serum.melt6, LI=variable)
LI_serum.melt6 <- rename(LI_serum.melt6, Concentration=value)
LI_serum.melt6$Time <- factor(LI_serum.melt6$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt6, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")+
theme(legend.position="none")
LI_serum.melt7 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('SM', 'CER'))
LI_serum.melt7<- dplyr::rename(LI_serum.melt7, LI=variable)
LI_serum.melt7 <- dplyr::rename(LI_serum.melt7, Concentration=value)
LI_serum.melt7$Time <- factor(LI_serum.melt7$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt7, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")+
theme(legend.position="none")
LI_serum.melt8 <- melt(LI_serum, id.vars = c('Time', 'Proband'), measure.vars = c('CER.SM'))
LI_serum.melt8 <- dplyr::rename(LI_serum.melt8, LI=variable)
LI_serum.melt8 <- dplyr::rename(LI_serum.melt8, Concentration=value)
ggplot(LI_serum.melt8,aes(x=Time, y=Concentration, fill= LI)) +
xlab ('Time Point') + ylab ('Concentration [nmol/ml]') +
geom_boxplot() +
scale_fill_manual(labels = c("CER/SM ratio"),
values = c("tomato")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
ggpaired(LI_serum.melt8, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'LI', short.panel.labs = FALSE) +
xlab('Timepoint') + ylab('CER/SM ratio') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ LI, scales="free")+
theme(legend.position="none")
LI_serum.melt9 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('LPC', 'PC'))
LI_serum.melt9<- dplyr::rename(LI_serum.melt9, LI=variable)
LI_serum.melt9 <- dplyr::rename(LI_serum.melt9, Concentration=value)
LI_serum.melt9$Time <- factor(LI_serum.melt9$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt9, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")+
theme(legend.position="none")
LI_serum.melt11 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('PC', 'PE'))
LI_serum.melt11<- dplyr::rename(LI_serum.melt11, LI=variable)
LI_serum.melt11 <- dplyr::rename(LI_serum.melt11, Concentration=value)
LI_serum.melt11$Time <- factor(LI_serum.melt11$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt11, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ Time, scales="free")+
theme(legend.position="none")
pdf("/Users/student05/Documents/fertige Plots/PC.PE.Korr2.pdf",width=8, height=10)
ggscatter(LI_serum, x='PC', y='PE', add = 'reg.line', cor.coef.coord = c(950, 80), cor.coef.size = 8,conf.int = TRUE,
cor.coef = TRUE,color = "grey59",fill = "lightgray", cor.method = 'spearman', xlab= 'Phosphatidylcholinkonzentrationen [nmol/ml]', ylab = 'Phosphatidylethanolaminkonzentrationen [nmol/ml]')+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text( hjust=1))+
geom_point(color='grey52')+
theme(legend.position="none")+
geom_point(color='black', size=2.5)
dev.off()
cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$PC, method = "spearman", exact = F)
cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$PCO, method = "spearman", exact = F)
cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$SM, method = "spearman", exact = F)
cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$PI, method = "spearman", exact = F)
cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$PEP, method = "spearman", exact = F)
cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$LPC, method = "spearman", exact = F)
cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$CER, method = "spearman", exact = F)
p.adjust(c(0.7706423 ,0.0001885,3.964e-07, 5.833e-16, 0.3947, 0.3421,0.007615 ), method = 'BH', n=7)
Plotten der Serumlipid-Verhältnisse
LI_serum.melt10 <- melt(LI_serum, id.vars = c('Time', 'Proband'), measure.vars = c('LPC.PC'))
LI_serum.melt10 <- dplyr::rename(LI_serum.melt10, LI=variable)
LI_serum.melt10 <- dplyr::rename(LI_serum.melt10, Concentration=value)
ggplot(LI_serum.melt10,aes(x=Time, y=Concentration, fill= LI)) +
xlab ('Time Point') + ylab ('Concentration [nmol/ml]') +
geom_boxplot() +
scale_fill_manual(labels = c("LPC/PC ratio"),
values = c("steelblue")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
ggpaired(LI_serum.melt10, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'LI', short.panel.labs = FALSE) +
xlab('Timepoint') + ylab('LPC/PC ratio') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ LI, scales="free")+
theme(legend.position="none")
LI_serum.melt12 <- melt(LI_serum, id.vars = 'Time', measure.vars = c('PC.PE'))
LI_serum.melt12 <- dplyr::rename(LI_serum.melt12, LI=variable)
LI_serum.melt12 <- dplyr::rename(LI_serum.melt12, Concentration=value)
ggplot(LI_serum.melt12,aes(x=Time, y=Concentration, fill= LI)) +
xlab ('Time Point') + ylab ('Concentration [nmol/ml]') +
geom_boxplot() +
scale_fill_manual(labels = c("PC/PE ratio"),
values = c("darkgreen")) +
stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI_serum.melt5 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('PC.PE'))
LI_serum.melt5<- dplyr::rename(LI_serum.melt5, LI=variable)
LI_serum.melt5 <- dplyr::rename(LI_serum.melt5, Concentration=value)
LI_serum.melt5$Time <- factor(LI_serum.melt5$Time, levels = c("PRE", "POST"))
ggpaired(LI_serum.melt5, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'LI', short.panel.labs = FALSE) +
xlab('Timepoint') + ylab('PC/PE ratio') +
geom_text(aes(label=Proband),hjust=0, vjust=0)+
facet_grid(.~ LI, scales="free")+
theme(legend.position="none")
6.2 Korrelationsanalysen zwischen Ceramid und Omega6-FA
Laden und filtern der Metadaten
LI_CER6 <- read.table("/Users/student05/Documents/omegga6:cer.txt", sep = '\t', comment='',head=T)
LI_CER6$Time <- factor(LI_CER6$Time, levels =c("PRE", "POST"))
LI_CER6 <- subset(filter(LI_CER6, !Proband == "33MP"))
Plotten der Korrelationen
In Arbeit
pdf("/Users/student05/Documents/fertige Plots/Ceramid.Linolsäure.pdf",width=8.5, height=10)
ggscatter(LI_CER6, x='CER', y='Linolsaeure_mol',color = 'Time',size = 2.5, palette = c('skyblue', 'orchid'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(8, 1000), cor.coef.size = 8, xlab= 'Ceramid Konzentrationen [nmol/ml]', ylab = 'Fäkale Linolsäurekonzentrationen [nmol/g]')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
dev.off()
ggscatter(LI_CER6, x='CER', y='Linolsaeure_mol', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'serum ceramide concentration [nmol/ml]', ylab = 'fecal linoleic fatty acid concentration [nmol/g]')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(LI_CER6, x='CER', y='Linolsaeure_i',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'serum CER concentration [nmol/ml]', ylab = 'fecal linoleic fatty acid concentration [nmol/g]')+
facet_grid(.~ Time)+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
6.3 Erstellen einer Korrelationsmatrix zum testen von Korrelationen zwischen den Serumlipiden
Filtern für PRE und POST
LI_serum_matrix_PRE <- subset(filter(LI_serum, Time == "PRE"))[ ,7:26]
LI_serum_matrix_POST <- subset(filter(LI_serum, Time == "POST"))[ ,7:26]
res.PRE <- cor(LI_serum_matrix_PRE)
res.POST <- cor(LI_serum_matrix_POST)
Spearman-Rangkorrelation und hinzufügen von Korrelationkoeffizient und p-value
res2.PRE <- rcorr(as.matrix(LI_serum_matrix_PRE), type = "spearman")
res2.POST <- rcorr(as.matrix(LI_serum_matrix_POST), type = "spearman")
res2.PRE$r
res2.POST$r
LI_serum_PRE_CC <- as.matrix((res2.PRE$r))
LI_serum_POST_CC <- as.matrix(res2.POST$r)
res2$P
LI_serum_PRE_PV <- as.matrix(res2.PRE$P)
LI_serum_POST_PV <- as.matrix(res2.POST$P)
Erstellen einer flattenCorrMatrix für PRE und POST
flattenCorrMatrix.PRE <- function(LI_serum_PRE_CC, LI_serum_PRE_PV) {
ut <- upper.tri(LI_serum_PRE_CC)
data.frame(
row = rownames(LI_serum_PRE_CC)[row(LI_serum_PRE_CC)[ut]],
column = rownames(LI_serum_PRE_CC)[col(LI_serum_PRE_CC)[ut]],
cor =(LI_serum_PRE_CC)[ut],
p = LI_serum_PRE_PV[ut]
)
}
flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P)
flattenCorrMatrix.POST <- function(LI_serum_POST_CC, LI_serum_POST_PV) {
ut <- upper.tri(LI_serum_POST_CC)
data.frame(
row = rownames(LI_serum_POST_CC)[row(LI_serum_POST_CC)[ut]],
column = rownames(LI_serum_POST_CC)[col(LI_serum_POST_CC)[ut]],
cor =(LI_serum_POST_CC)[ut],
p = LI_serum_POST_PV[ut]
)
}
flattenCorrMatrix.POST(res2.POST$r, res2.POST$P)
Dataframe erstellen
LI_PRE_cor.p <- as.data.frame(flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P))
LI_POST_cor.p <- as.data.frame(flattenCorrMatrix.POST(res2.POST$r, res2.POST$P))
colnames(LI_PRE_cor.p) <- c("LI", "LI", "correlation coefficient", "p-value")
colnames(LI_POST_cor.p) <- c("LI", "LI", "correlation coefficient", "p-value")
Corrplot erstellen zu den Zeiten PRE und POST
corrplot(res.PRE, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
corrplot(res.POST, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
corrplot(res2.PRE$r, type="upper", order="hclust",
p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
corrplot(res2.PRE$r, type="upper", order="hclust",
p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
Scatterplots erstellen zu den Zeiten PRE und POST
chart.Correlation(LI_serum_matrix_PRE, histogram=TRUE, pch=19)
chart.Correlation(SCFA_stool_matrix_POST, histogram = T, pch = 19)
6.4 Unterschiede der Serumlipidkonzentrationen zwischen Sterolkonvertierungstypen
Laden und filtern der Daten Lipidmetadaten s.o. In high und low converter unterteilen
lowconv <- filter(LI_serum, Proband == "05AP" | Proband == "33MP"
| Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
| Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")
lowconv['Phenotype'] = 'low converter'
highconv <- filter(LI_serum, Proband == "06WT" | Proband == "07RW"
| Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
| Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
| Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
| Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
highconv['Phenotype'] = 'high converter'
highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL
noconv <- filter(LI_serum, Proband == "28HM" | Proband == "32FG"
| Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
| Proband == "39DA" | Proband == "66DG" | Proband == "70PL")
noconv['Phenotype'] = 'not classified'
noconv$Converter.Type <- NULL
convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)
convT_paired <- filter(convT, Proband == "05AP" | Proband == "06WT"
| Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
| Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
| Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
| Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
| Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
| Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
| Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
| Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
Plotten der Unterschiede der Serumlipidkonzentrationen zwischen den Sterolkonvertierungstypen
LI_serum.melt <- melt(convT_paired, id.vars = c('Phenotype', 'Time'), measure.vars = c('PC', 'PCO', 'SM', 'PE', 'PI','PEP', 'LPC', 'CER', 'HexCer'))
LI_serum.melt <- subset(filter(LI_serum.melt, !Phenotype == "not classified"))
LI_serum.melt <- rename(LI_serum.melt, variable=LI)
LI_serum.melt <- rename(LI_serum.melt, Concentration=value)
ggplot(LI_serum.melt,aes(x=Phenotype, y=value, fill= variable)) +
xlab ('Converter type') + ylab ('Concentration [nmol/ml]') +
geom_boxplot()+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
facet_grid(.~Time)+
theme(legend.position="top")
LI_serum.melt1 <- melt(convT_paired, id.vars = c('Phenotype', 'Time'), measure.vars = c('Sum', 'Sum.Membrane','Sum.Storage', 'Sum.Lyso'))
LI_serum.melt1 <- subset(filter(LI_serum.melt1, !Phenotype == "not classified"))
LI_serum.melt1 <- rename(LI_serum.melt1, variable=LI)
LI_serum.melt1 <- rename(LI_serum.melt1, Concentration=value)
ggplot(LI_serum.melt1,aes(x=Phenotype, y=value, fill= variable)) +
xlab ('Converter type') + ylab ('Concentration [nmol/ml]') +
geom_boxplot()+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
facet_grid(.~Time)+
theme(legend.position="top")+
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))
Plotten der Unterschiede der Serumlipidverhältnisse zwischen den Sterolkonvertierungstypen
LI.r1 <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('LPC.PC'))
ggplot(filter(LI.r1, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("LPC/PC"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.r2 <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('CER.SM'))
ggplot(filter(LI.r2, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("CER/SM"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI_serum.melt2 <- melt(convT_paired, id.vars = c('Phenotype', 'Time'), measure.vars = c( 'PC.PE'))
LI_serum.melt2 <- subset(filter(LI_serum.melt2, !Phenotype == "not classified"))
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, variable= LI)
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, Concentration=value)
comparison_conv <- list(c("low converter", "high converter"))
comparison_time <- list(c("PRE", "POST"))
ggplot(LI_serum.melt2,aes(x=Phenotype, y=value, fill= variable)) +
xlab ('Converter type') + ylab ('PC/PE ratio') +
geom_boxplot()+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=45, hjust=1))+
facet_grid(.~Time)+
theme(legend.position="none")+
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))
ggplot(LI_serum.melt2,aes(x=Time, y=value, fill= variable)) +
xlab ('Converter type') + ylab ('PC/PE ratio') +
geom_boxplot()+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=0, hjust=1))+
facet_grid(.~Phenotype)+
theme(legend.position="none")+
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))
Wilcoxon-Test, mean und SD, Plotten der Unterschiede des PC/PE-Verhältnisses zwischen den Sterolkonvertierungstypen In Arbeit
mean(subset(filter(convT_paired, Time == "PRE" & Phenotype == "high converter"))$PC.PE)
sd(subset(filter(convT_paired, Time == "PRE" & Phenotype == "high converter"))$PC.PE)
mean(subset(filter(convT_paired, Time == "POST" & Phenotype == "high converter"))$PC.PE)
sd(subset(filter(convT_paired, Time == "POST" & Phenotype == "high converter"))$PC.PE)
mean(subset(filter(convT_paired, Time == "PRE" & Phenotype == "low converter"))$PC.PE)
sd(subset(filter(convT_paired, Time == "PRE" & Phenotype == "low converter"))$PC.PE)
mean(subset(filter(convT_paired, Time == "POST" & Phenotype == "low converter"))$PC.PE)
sd(subset(filter(convT_paired, Time == "POST" & Phenotype == "low converter"))$PC.PE)
pairwise.wilcox.test(subset(filter(convT_paired, Time == "PRE"))$PC.PE, subset(filter(convT_paired, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired, Time == "POST"))$PC.PE, subset(filter(convT_paired, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired, Phenotype == "low converter"))$PC.PE, subset(filter(convT_paired, Phenotype == "low converter"))$Time, p.adjust.method = 'BH', paired = F)
pairwise.wilcox.test(subset(filter(convT_paired, Phenotype == "high converter"))$PC.PE, subset(filter(convT_paired, Phenotype == "high converter"))$Time, p.adjust.method = 'BH', paired = F)
LI.r2 <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PC.PE'))
LI.r2 <- rename( LI.r2, LI=variable)
LI.r2 <- rename( LI.r2, Concentration=value)
pdf("/Users/student05/Documents/fertige Plots/converter.PC.PE.pdf",width=8, height=10)
ggplot(filter(LI.r2, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= Phenotype)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Phosphatidylcholin/Phosphatidylethanolamin Verhältnis') +
scale_fill_manual(labels=c("high converter", "low converter"), values = c("seashell4", "seashell2"))+
geom_boxplot(width = .7, lwd=0.6) + theme_classic() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("low converter")))+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18, colour = "black"),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
dev.off()
ggplot(filter(LI.r2, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("PC/PE"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
Plotten der Unterschiede einzelner Serumlipidkonzentrationen zwischen den Sterolkonvertierungstypen
LI.PCO <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PCO'))
LI.PCO <- rename(LI.PCO, FA=variable)
LI.PCO <- rename(LI.PCO, Concentration=value)
ggplot(filter(LI.PCO, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("PCO"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.PCO, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("PCO"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.SM <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('SM'))
LI.SM <- rename(LI.SM, FA=variable)
LI.SM <- rename(LI.SM, Concentration=value)
ggplot(filter(LI.SM, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("SM"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.SM, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("SM"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.PE <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PE'))
LI.PE <- rename(LI.PE, FA=variable)
LI.PE <- rename(LI.PE, Concentration=value)
ggplot(filter(LI.PE, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("PE"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.SM, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("SM"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.PI <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PI'))
LI.PI <- rename(LI.PI, FA=variable)
LI.PI <- rename(LI.PI, Concentration=value)
ggplot(filter(LI.PI, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("PI"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.PI, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("PI"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.PEP <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PEP'))
LI.PEP <- rename(LI.PEP, FA=variable)
LI.PEP <- rename(LI.PEP, Concentration=value)
ggplot(filter(LI.PEP, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("PEP"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.PEP, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("PEP"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.LPC <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('LPC'))
LI.LPC <- rename(LI.LPC, FA=variable)
LI.LPC <- rename(LI.LPC, Concentration=value)
ggplot(filter(LI.LPC, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("LPC"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.LPC, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("LPC"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.CER <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('CER'))
ggplot(filter(LI.CER, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("CER"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.LPC, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("LPC"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.HexCer <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('HexCer'))
ggplot(filter(LI.HexCer, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("HEXCER"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.HexCer, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("HexCer"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.Sum <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('Sum'))
ggplot(filter(LI.Sum, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("SUM"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.HexCer, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("HexCer"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.Sum.m <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('Sum.Membrane'))
ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("SUM Membrane"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.Sum.s <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('Sum.Storage'))
ggplot(filter(LI.Sum.s, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("SUM Storage"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
LI.Sum.l <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('Sum.Lyso'))
ggplot(filter(LI.Sum.l, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
facet_grid(.~ Time) +
xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') +
scale_fill_manual(labels=c("SUM Lyso"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="top")
ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
facet_grid(.~ Phenotype) +
xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') +
scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
geom_boxplot() +
stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
theme(legend.position="top")
6.4 alpha-Diversitätsanalysen mit Serumlipide
Laden, filtern und synchronisieren der Metadaten
map_alphadiv <- read.table("/Users/student05/Downloads/means_alpha_div.txt", sep = '\t', comment='',head = TRUE, row.names = 1)
LI_serum <- read.table("/Users/student05/Documents/serum lipids zahlen.1-2.txt", sep = '\t', comment='',head=T)
LI_serum$Time <-factor(LI_serum$Time, levels = c("PRE", "POST"))
row.names(LI_serum) <- LI_serum$SampleID
row.names(map_alphadiv)
common.ids.St <- intersect(rownames(LI_serum), rownames(map_alphadiv))
common.ids.St <- intersect(row.names(LI_serum), row.names(map_alphadiv))
LI_serum <- LI_serum[common.ids.St,]
map_alphadiv <- map_alphadiv[common.ids.St,]
LI_serum$Shannon <- map_alphadiv$Shannon
LI_serum$Simpson <- map_alphadiv$Simpson
Loop für Korrelationsanalyse zwischen Shannon-Index und Serumlipiden
corr_colnames_LI <-colnames(LI_serum[,7:26])
corr_spearman_Shannon_LI <- data.frame()
for( i in unique(corr_colnames_LI)) {
tmp <- filter(LI_serum, !is.na(i))
x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y = t(as.matrix(tmp$Shannon) )
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
w = t(as.matrix(subset(filter(tmp, Time == "PRE"))$Shannon))
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Shannon))
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Shannon_LI)+1
corr_spearman_Shannon_LI[nrow,"Div"] = "Shannon"
corr_spearman_Shannon_LI[nrow, "column"] = i
corr_spearman_Shannon_LI[nrow, "rho"] = rho
corr_spearman_Shannon_LI[nrow, "p.value"] = p
corr_spearman_Shannon_LI[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Shannon_LI[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Shannon_LI[nrow, "rho_POST"] = rho_POST
corr_spearman_Shannon_LI[nrow, "p.value_POST"] = p_POST
}
corr_spearman_Shannon_LI$p.adjusted <- p.adjust(corr_spearman_Shannon_LI$p.value,method = "BH", n = 20)
corr_spearman_Shannon_LI$p.adjusted_PRE <-p.adjust(corr_spearman_Shannon_LI$p.value_PRE, method = "BH", n = 20)
corr_spearman_Shannon_LI$p.adjusted_POST <- p.adjust(corr_spearman_Shannon_LI$p.value_POST, method = "BH", n = 20)
corr_spearman_Shannon_LI$p.adjusted_FU <- p.adjust(corr_spearman_Shannon_LI$p.value_FU, method = "BH", n = 20)
write.table(corr_spearman_Shannon_LI, file = '/Users/student05/Documents/serum lipids/diversity/LI.Shannon.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
Plotten von Korrelationen zwischen Shannon-Index und Serumlipiden
alle Lipide
LI_serum.melt <- melt(LI_serum, id.vars = c('Time','Shannon'), measure.vars = c( 'PC', 'PCO', 'SM', 'PE', 'PI','PEP', 'LPC', 'CER', 'HexCer'))
LI_serum.melt <- dplyr::rename(LI_serum.melt, LI=variable)
LI_serum.melt <- dplyr::rename(LI_serum.melt, Concentration=value)
LI_serum.melt.pr <- subset(filter(LI_serum.melt, !Time =='POST'))
LI_serum.melt.po <- subset(filter(LI_serum.melt, !Time =='PRE'))
ggscatter(LI_serum.melt.pr, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2', 'deeppink', 'brown4', 'darkorange1', 'blueviolet', 'aquamarine3'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c( 100, 7), xlab= 'serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
facet_grid(.~ LI,scales = "free_x")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(LI_serum.melt, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2', 'deeppink', 'brown4', 'darkorange1', 'blueviolet', 'aquamarine3'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
facet_grid(.~ LI,scales = "free_x")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(LI_serum.melt.po, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2', 'deeppink', 'brown4', 'darkorange1', 'blueviolet', 'aquamarine3'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE,cor.coef.coord = c(NULL, NULL),
cor.method = 'spearman', xlab= 'serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
facet_grid(.~ LI,scales = "free_x")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Summierten Lipidkonzentrationen
LI_serum.melt1 <- melt(LI_serum, id.vars = c('Time','Shannon'), measure.vars = c( 'Sum', 'Sum.Membrane','Sum.Storage', 'Sum.Lyso'))
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, LI=variable)
LI_serum.melt1<- dplyr::rename(LI_serum.melt1, Concentration=value)
LI_serum.melt1.pr <- subset(filter(LI_serum.melt1, !Time =='POST'))
LI_serum.melt1.po <- subset(filter(LI_serum.melt1, !Time =='PRE'))
ggscatter(LI_serum.melt1.pr, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, 7), xlab= 'sum serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
facet_grid(.~ LI,scales = "free_x")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(LI_serum.melt1.po, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
facet_grid(.~ LI,scales = "free_x")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Lipidverhältnisse
LI_serum.melt2 <- melt(LI_serum, id.vars = c('Time','Shannon'), measure.vars = c( 'LPC.PC','CER.SM', 'PC.PE'))
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, LI=variable)
LI_serum.melt2<- dplyr::rename(LI_serum.melt2, Concentration=value)
LI_serum.melt2.pr <- subset(filter(LI_serum.melt2, !Time =='POST'))
LI_serum.melt2.po <- subset(filter(LI_serum.melt2, !Time =='PRE'))
ggscatter(LI_serum.melt2.pr, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0.03, 7), xlab= 'ratio serum lipids', ylab = 'Shannon-Index')+
facet_grid(.~ LI,scales = "free_x")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(LI_serum.melt2.po, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0.03, 7), xlab= 'ratio serum lipids', ylab = 'Shannon-Index')+
facet_grid(.~ LI,scales = "free_x")+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(LI_serum, x='PC.PE', y='Shannon',color = 'Time', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(40, 7),cor.coef.size = 6, xlab= 'PC/PE serum lipid ratio', ylab = 'Shannon-Index')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
ggscatter(LI_serum, x='PC.PE', y='Shannon', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(40, 7),cor.coef.size = 6, xlab= 'PC/PE serum lipid ratio', ylab = 'Shannon-Index')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
Loop für Korrelationsanalyse zwischen Simpson-Index und Serumlipiden
corr_spearman_Simpson_LI <- data.frame()
for( i in unique(corr_colnames_LI)) {
tmp <- filter(LI_serum, !is.na(i))
x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
y = t(as.matrix(tmp$Simpson))
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
w = t(as.matrix (subset(filter(tmp, Time == "PRE"))$Simpson))
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Simpson))
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Simpson_LI)+1
corr_spearman_Simpson_LI[nrow,"Div"] = "Simpson"
corr_spearman_Simpson_LI[nrow, "column"] = i
corr_spearman_Simpson_LI[nrow, "rho"] = rho
corr_spearman_Simpson_LI[nrow, "p.value"] = p
corr_spearman_Simpson_LI[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Simpson_LI[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Simpson_LI[nrow, "rho_POST"] = rho_POST
corr_spearman_Simpson_LI[nrow, "p.value_POST"] = p_POST
}
corr_spearman_Simpson_LI$p.adjusted <- p.adjust(corr_spearman_Simpson_LI$p.value,method = "BH", n = 20)
corr_spearman_Simpson_LI$p.adjusted_PRE <-p.adjust(corr_spearman_Simpson_LI$p.value_PRE, method = "BH", n = 20)
corr_spearman_Simpson_LI$p.adjusted_POST <- p.adjust(corr_spearman_Simpson_LI$p.value_POST, method = "BH", n = 20)
corr_spearman_Simpson_LI$p.adjusted_FU <- p.adjust(corr_spearman_Simpson_LI$p.value_FU, method = "BH", n = 20)
write.table(corr_spearman_Simpson_LI, file = '/Users/student05/Documents/serum lipids/diversity/LI.Simpson.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
-> gleicher Effekt wie bei Shannon-Index
6.5 Korrelationsanalysen zwischen dem relativen Vorkommen von Taxa und Serumlipiden
Laden und filtern der Metadaten
map_KD <- read.table("/Users/student05/Documents/txt dateien r/Mappingfile_16SrRNA_BC22.txt", sep ='\t', comment='', head=T,row.names = 1)
relab <- read.table("/Users/student05/Documents/relative abundance/L6_metadata_taxa_strict_stool.txt", sep = '\t', comment='', head=T)
relab_PRE <- filter(relab, Time == "PRE")
relab_POST <- filter(relab, Time == "POST")
relab_FU <- filter(relab, Time == "FOLLOW-UP")
relab_means_PRE <- aggregate(relab_PRE[, 10:90], list(relab_PRE$Proband), mean)
relab_means_PRE['Time'] = 'PRE'
relab_means_PRE <- dplyr::rename(relab_means_PRE, Proband=Group.1)
relab_means_POST <- aggregate(relab_POST[, 10:90], list(relab_POST$Proband), mean)
relab_means_POST['Time'] = 'POST'
relab_means_POST <- dplyr::rename(relab_means_POST, Proband=Group.1)
relab_means_FU <- aggregate(relab_FU[, 10:90], list(relab_FU$Proband), mean)
relab_means_FU['Time'] = 'FOLLOW-UP'
relab_means_FU <- dplyr::rename(relab_means_FU, Proband=Group.1)
relab_means <- data_frame()
relab_means <- bind_rows(relab_means_PRE, relab_means_POST, relab_means_FU)
relab_means <- relab_means[, c(1, 83, 2:82)]
relab_means_melt <- melt(relab_means, id=c('Proband', 'Time'))
relab_means_melt <- dplyr::rename(relab_means_melt, Taxa=variable)
relab_means_melt <- dplyr::rename(relab_means_melt, Relative_Abundance=value)
relab_phylum <- subset(relab_means_melt, !grepl("g__|f__|o__|c__", relab_means_melt$Taxa))
relab_phylum <- subset(relab_phylum, !grepl("k__Archaea", relab_phylum$Taxa))
relab_phylum$Time <- factor(relab_phylum$Time, levels=c('PRE','POST','FOLLOW-UP'))
relab_phylum_spread <- spread(relab_phylum, Taxa, Relative_Abundance, sep = NULL)
relab_genus <- subset(relab_means_melt, grepl("g__", relab_means_melt$Taxa))
relab_genus <- subset(relab_genus, !grepl("k__Archaea", relab_genus$Taxa))
relab_genus$Time <- factor(relab_genus$Time, levels = c('PRE','POST','FOLLOW-UP'))
relab_genus_spread <- spread(relab_genus, Taxa, Relative_Abundance, sep = NULL)
Laden der Serumlipidmetadaten, Synchonisieren der Metadaten
LI_serum <- read.table("/Users/student05/Documents/serum lipids zahlen.1-2.txt", sep = '\t', comment='',head=T)
LI_serum$Time <-factor(LI_serum$Time, levels = c("PRE", "POST"))
relab_phylum_ID <- relab_phylum_spread
relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))
row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID
relab_genus_ID <- relab_genus_spread
relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))
row.names(relab_genus_ID) <- relab_genus_ID$SampleID
LI_serum <- mutate(LI_serum, SampleID1 = paste(Proband, Time, sep = "."))
row.names(LI_serum) <- LI_serum$SampleID1
common.ids.relab <- intersect(rownames(LI_serum), rownames(relab_phylum_ID))
LI_serum <- LI_serum[common.ids.relab,]
relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]
relab_genus_ID <- relab_genus_ID[common.ids.relab,]
Subsetten des Phylum-levels, log-Transformation und hinzufühen von Pseudocount 0.00001 Filtern nach Proben mit PRE und POST
phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])
relab_phylum_ID1 <- relab_phylum_ID[,c(3:8)] + 0.00001
relab_phylum_ID_log <- log10(relab_phylum_ID_log)
phylum_LI <- cbind(relab_phylum_ID1, LI_serum[, c(1:27)])
phylum_LI$Proband
phylum_LI <- subset(filter(phylum_LI, !Proband == '31KE'))
phylum_LI <- subset(filter(phylum_LI, !Proband == '45GL'))
phylum_LI <- subset(filter(phylum_LI, !Proband == '34WF'))
phylum_LI <- subset(filter(phylum_LI, !Proband == '54SL'))
phylum_LI <- subset(filter(phylum_LI, !Proband == '74SA'))
phylum_LI$Time <- factor(phylum_LI$Time, levels = c("PRE", "POST"))
Loop und Plots Korrelation zwischen Phosphatidylcholin und phylum-level
In Arbeit
corr_map_phylum_PC <- filter(phylum_LI, !is.na(PC))
corr_spearman_Phylum_PC <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_PC, !is.na(i))
y = tmp[,i]
x = tmp$PC
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PC
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PC
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_PC)+1
corr_spearman_Phylum_PC[nrow,"FA"] <- "PC"
corr_spearman_Phylum_PC[nrow, "Phylum"] = i
corr_spearman_Phylum_PC[nrow, "p.value"] = p
corr_spearman_Phylum_PC[nrow, "rho"] = rho
corr_spearman_Phylum_PC[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_PC[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_PC[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_PC[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_PC$p.adjusted <- p.adjust(corr_spearman_Phylum_PC$p.value, method = "BH", n = 35)
corr_spearman_Phylum_PC$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PC$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_PC$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PC$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_PC, file = '/Users/student05/Documents/serum lipids/phylum/PC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Phosphatidylcholinkonzentrationen [nmol/ml]', ylab = 'log10 (Relatives Vorkommen p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
pdf("/Users/student05/Documents/fertige Plots/PC.Proteo.pdf",width=8, height=10)
ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('skyblue', 'orchid'),size = 2.5, add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(1000, -1.5), cor.coef.size = 8,xlab= 'Phosphatidylcholinkonzentrationen [nmol/ml]', ylab = 'Relatives Vorkommen p__Proteobacteria [%]')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
scale_y_log10(labels = percent_format())+
theme(legend.position="none")
dev.off()
ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Proteobacteria', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(1000, -1.5), cor.coef.size = 5,xlab= 'PC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Phosphatidylcholin-Plasmalogen und phylum-level
corr_map_phylum_PCO <- filter(phylum_LI, !is.na(PCO))
corr_spearman_Phylum_PCO <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_PCO, !is.na(i))
y = tmp[,i]
x = tmp$PCO
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PCO
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PCO
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_PCO)+1
corr_spearman_Phylum_PCO[nrow,"FA"] <- "PCO"
corr_spearman_Phylum_PCO[nrow, "Phylum"] = i
corr_spearman_Phylum_PCO[nrow, "p.value"] = p
corr_spearman_Phylum_PCO[nrow, "rho"] = rho
corr_spearman_Phylum_PCO[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_PCO[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_PCO[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_PCO[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_PCO$p.adjusted <- p.adjust(corr_spearman_Phylum_PCO$p.value, method = "BH", n = 35)
corr_spearman_Phylum_PCO$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PCO$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_PCO$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PCO$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_PCO, file = '/Users/student05/Documents/serum lipids/phylum/PCO.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Phosphatidylethanolamin und phylum-level
In Arbeit
corr_map_phylum_PE <- filter(phylum_LI, !is.na(PE))
corr_spearman_Phylum_PE <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_PE, !is.na(i))
y = tmp[,i]
x = tmp$PE
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PE
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PE
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_PE)+1
corr_spearman_Phylum_PE[nrow,"FA"] <- "PE"
corr_spearman_Phylum_PE[nrow, "Phylum"] = i
corr_spearman_Phylum_PE[nrow, "p.value"] = p
corr_spearman_Phylum_PE[nrow, "rho"] = rho
corr_spearman_Phylum_PE[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_PE[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_PE[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_PE[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_PE$p.adjusted <- p.adjust(corr_spearman_Phylum_PE$p.value, method = "BH", n = 35)
corr_spearman_Phylum_PE$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PE$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_PE$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PE$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_PE, file = '/Users/student05/Documents/serum lipids/phylum/PE.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(10, -0.9), cor.coef.size = 5, xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(10, -0.9), cor.coef.size = 5, xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
pdf("/Users/student05/Documents/fertige Plots/PE.proteo.pdf",width=8.5, height=10)
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('skyblue', 'orchid'), size = 2.5, add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(15, -1.5), cor.coef.size = 8,xlab= 'Phosphatidylethanolaminkonzentrationen [nmol/ml]', ylab = 'Relatives Vorkommen p__Proteobacteria [%]')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
scale_y_log10(labels = percent_format())+
theme(legend.position="none")
dev.off()
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Proteobacteria', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]',cor.coef.coord = c(20, -1.4), cor.coef.size = 5, ylab = 'log10 (Relative Abundance p__Proteobacteria)')+theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Sphingomyelin und phylum-level
corr_map_phylum_SM <- filter(phylum_LI, !is.na(SM))
corr_spearman_Phylum_SM <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_SM, !is.na(i))
y = tmp[,i]
x = tmp$SM
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$SM
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$SM
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_SM)+1
corr_spearman_Phylum_SM[nrow,"FA"] <- "SM"
corr_spearman_Phylum_SM[nrow, "Phylum"] = i
corr_spearman_Phylum_SM[nrow, "p.value"] = p
corr_spearman_Phylum_SM[nrow, "rho"] = rho
corr_spearman_Phylum_SM[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_SM[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_SM[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_SM[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_SM$p.adjusted <- p.adjust(corr_spearman_Phylum_SM$p.value, method = "BH", n = 35)
corr_spearman_Phylum_SM$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_SM$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_SM$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_SM$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_SM, file = '/Users/student05/Documents/serum lipids/phylum/SM.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipids concentration [nmol/ml]',cor.coef.coord = c(200, -0.9), cor.coef.size = 4, ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(angle=45, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipids concentration [nmol/ml]',cor.coef.coord = c(200, -0.9), cor.coef.size = 4, ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Phosphatidylinositol und phylum-level
corr_map_phylum_PI <- filter(phylum_LI, !is.na(PI))
corr_spearman_Phylum_PI <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_PI, !is.na(i))
y = tmp[,i]
x = tmp$PI
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PI
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PI
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_PI)+1
corr_spearman_Phylum_PI[nrow,"FA"] <- "PI"
corr_spearman_Phylum_PI[nrow, "Phylum"] = i
corr_spearman_Phylum_PI[nrow, "p.value"] = p
corr_spearman_Phylum_PI[nrow, "rho"] = rho
corr_spearman_Phylum_PI[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_PI[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_PI[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_PI[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_PI$p.adjusted <- p.adjust(corr_spearman_Phylum_PI$p.value, method = "BH", n = 35)
corr_spearman_Phylum_PI$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PI$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_PI$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PI$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_PI, file = '/Users/student05/Documents/serum lipids/phylum/PI.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Phosphatidylethanolamin-Plasmalogen und phylum-level
corr_map_phylum_PEP <- filter(phylum_LI, !is.na(PEP))
corr_spearman_Phylum_PEP <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_PEP, !is.na(i))
y = tmp[,i]
x = tmp$PEP
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PEP
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PEP
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_PEP)+1
corr_spearman_Phylum_PEP[nrow,"FA"] <- "PEP"
corr_spearman_Phylum_PEP[nrow, "Phylum"] = i
corr_spearman_Phylum_PEP[nrow, "p.value"] = p
corr_spearman_Phylum_PEP[nrow, "rho"] = rho
corr_spearman_Phylum_PEP[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_PEP[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_PEP[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_PEP[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_PEP$p.adjusted <- p.adjust(corr_spearman_Phylum_PEP$p.value, method = "BH", n = 35)
corr_spearman_Phylum_PEP$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PEP$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_PEP$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PEP$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_PEP, file = '/Users/student05/Documents/serum lipids/phylum/PEP.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Lysophosphatidylcholin und phylum-level
corr_map_phylum_LPC <- filter(phylum_LI, !is.na(LPC))
corr_spearman_Phylum_LPC <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_LPC, !is.na(i))
y = tmp[,i]
x = tmp$LPC
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$LPC
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$LPC
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_LPC)+1
corr_spearman_Phylum_LPC[nrow,"FA"] <- "LPC"
corr_spearman_Phylum_LPC[nrow, "Phylum"] = i
corr_spearman_Phylum_LPC[nrow, "p.value"] = p
corr_spearman_Phylum_LPC[nrow, "rho"] = rho
corr_spearman_Phylum_LPC[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_LPC[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_LPC[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_LPC[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_LPC$p.adjusted <- p.adjust(corr_spearman_Phylum_LPC$p.value, method = "BH", n = 35)
corr_spearman_Phylum_LPC$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_LPC$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_LPC$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_LPC$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_LPC, file = '/Users/student05/Documents/serum lipids/phylum/LPC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Ceramid und phylum-level
corr_map_phylum_CER <- filter(phylum_LI, !is.na(CER))
corr_spearman_Phylum_CER <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_CER, !is.na(i))
y = tmp[,i]
x = tmp$CER
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$CER
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$CER
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_CER)+1
corr_spearman_Phylum_CER[nrow,"FA"] <- "CER"
corr_spearman_Phylum_CER[nrow, "Phylum"] = i
corr_spearman_Phylum_CER[nrow, "p.value"] = p
corr_spearman_Phylum_CER[nrow, "rho"] = rho
corr_spearman_Phylum_CER[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_CER[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_CER[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_CER[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_CER$p.adjusted <- p.adjust(corr_spearman_Phylum_CER$p.value, method = "BH", n = 35)
corr_spearman_Phylum_CER$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_CER$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_CER$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_CER$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_CER, file = '/Users/student05/Documents/serum lipids/phylum/CER.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Cer serum lipids concentration [nmol/ml]', cor.coef.coord = c(8, -0.9), cor.coef.size = 6,ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Cer serum lipids concentration [nmol/ml]', cor.coef.coord = c(8, -0.9), cor.coef.size = 6,ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Cer serum lipids concentration [nmol/ml]',cor.coef.coord = c(8, -0.9), cor.coef.size = 6, ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Hexosylceramid und phylum-level
corr_map_phylum_HexCer <- filter(phylum_LI, !is.na(HexCer))
corr_spearman_Phylum_HexCer <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_HexCer, !is.na(i))
y = tmp[,i]
x = tmp$HexCer
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$HexCer
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$HexCer
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_HexCer)+1
corr_spearman_Phylum_HexCer[nrow,"FA"] <- "HexCer"
corr_spearman_Phylum_HexCer[nrow, "Phylum"] = i
corr_spearman_Phylum_HexCer[nrow, "p.value"] = p
corr_spearman_Phylum_HexCer[nrow, "rho"] = rho
corr_spearman_Phylum_HexCer[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_HexCer[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_HexCer[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_HexCer[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_HexCer$p.adjusted <- p.adjust(corr_spearman_Phylum_HexCer$p.value, method = "BH", n = 35)
corr_spearman_Phylum_HexCer$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_HexCer$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_HexCer$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_HexCer$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_HexCer, file = '/Users/student05/Documents/serum lipids/phylum/HexCer.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, label = 'Proband',
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundancep__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen summierten Serumlipiden und phylum-level
corr_map_phylum_Sum <- filter(phylum_LI, !is.na(Sum))
corr_spearman_Phylum_Sum <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Sum, !is.na(i))
y = tmp[,i]
x = tmp$Sum
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Sum
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Sum
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Sum)+1
corr_spearman_Phylum_Sum[nrow,"FA"] <- "Sum"
corr_spearman_Phylum_Sum[nrow, "Phylum"] = i
corr_spearman_Phylum_Sum[nrow, "p.value"] = p
corr_spearman_Phylum_Sum[nrow, "rho"] = rho
corr_spearman_Phylum_Sum[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Sum[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Sum[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Sum[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Sum$p.adjusted <- p.adjust(corr_spearman_Phylum_Sum$p.value, method = "BH", n = 35)
corr_spearman_Phylum_Sum$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Sum$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Sum$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Sum$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_Sum, file = '/Users/student05/Documents/serum lipids/phylum/Sum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen summierten Membranlipiden und phylum-level
corr_map_phylum_Sum.Membrane <- filter(phylum_LI, !is.na(Sum.Membrane))
corr_spearman_Phylum_Sum.Membrane <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Sum.Membrane, !is.na(i))
y = tmp[,i]
x = tmp$Sum.Membrane
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Sum.Membrane
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Sum.Membrane
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Sum.Membrane)+1
corr_spearman_Phylum_Sum.Membrane[nrow,"FA"] <- "Sum.Membrane"
corr_spearman_Phylum_Sum.Membrane[nrow, "Phylum"] = i
corr_spearman_Phylum_Sum.Membrane[nrow, "p.value"] = p
corr_spearman_Phylum_Sum.Membrane[nrow, "rho"] = rho
corr_spearman_Phylum_Sum.Membrane[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Sum.Membrane[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Sum.Membrane[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Sum.Membrane[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Sum.Membrane$p.adjusted <- p.adjust(corr_spearman_Phylum_Sum.Membrane$p.value, method = "BH", n = 35)
corr_spearman_Phylum_Sum.Membrane$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Sum.Membrane$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Sum.Membrane$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Sum.Membrane$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_Sum.Membrane, file = '/Users/student05/Documents/serum lipids/phylum/Sum.Membrane.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'Summarized membrane serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen summierten Storagelipiden und phylum-level
corr_map_phylum_Sum.Storage <- filter(phylum_LI, !is.na(Sum.Storage))
corr_spearman_Phylum_Sum.Storage <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Sum.Storage, !is.na(i))
y = tmp[,i]
x = tmp$Sum.Storage
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Sum.Storage
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Sum.Storage
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Sum.Storage)+1
corr_spearman_Phylum_Sum.Storage[nrow,"FA"] <- "Sum.Storage"
corr_spearman_Phylum_Sum.Storage[nrow, "Phylum"] = i
corr_spearman_Phylum_Sum.Storage[nrow, "p.value"] = p
corr_spearman_Phylum_Sum.Storage[nrow, "rho"] = rho
corr_spearman_Phylum_Sum.Storage[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Sum.Storage[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Sum.Storage[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Sum.Storage[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Sum.Storage$p.adjusted <- p.adjust(corr_spearman_Phylum_Sum.Storage$p.value, method = "BH", n = 35)
corr_spearman_Phylum_Sum.Storage$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Sum.Storage$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Sum.Storage$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Sum.Storage$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_Sum.Storage, file = '/Users/student05/Documents/serum lipids/phylum/Sum.Storage.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'summarized storage serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'summarized storage serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, or.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen summierten Lysolipiden und phylum-level
corr_map_phylum_Sum.Lyso <- filter(phylum_LI, !is.na(Sum.Lyso))
corr_spearman_Phylum_Sum.Lyso <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_Sum.Lyso, !is.na(i))
y = tmp[,i]
x = tmp$Sum.Lyso
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Sum.Lyso
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Sum.Lyso
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_Sum.Lyso)+1
corr_spearman_Phylum_Sum.Lyso[nrow,"FA"] <- "Sum.Lyso"
corr_spearman_Phylum_Sum.Lyso[nrow, "Phylum"] = i
corr_spearman_Phylum_Sum.Lyso[nrow, "p.value"] = p
corr_spearman_Phylum_Sum.Lyso[nrow, "rho"] = rho
corr_spearman_Phylum_Sum.Lyso[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_Sum.Lyso[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_Sum.Lyso[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_Sum.Lyso[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_Sum.Lyso$p.adjusted <- p.adjust(corr_spearman_Phylum_Sum.Lyso$p.value, method = "BH", n = 35)
corr_spearman_Phylum_Sum.Lyso$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Sum.Lyso$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Sum.Lyso$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Sum.Lyso$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_Sum.Lyso, file = '/Users/student05/Documents/serum lipids/phylum/Sum.Lyso.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'summarized lyso serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'summarized lyso serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und PLots Korrelation zwischen LPC/PC-Verhältnis und phylum-level
corr_map_phylum_LPC.PC <- filter(phylum_LI, !is.na(LPC.PC))
corr_spearman_Phylum_LPC.PC <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_LPC.PC, !is.na(i))
y = tmp[,i]
x = tmp$LPC.PC
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$LPC.PC
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$LPC.PC
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_LPC.PC)+1
corr_spearman_Phylum_LPC.PC[nrow,"FA"] <- "LPC.PC"
corr_spearman_Phylum_LPC.PC[nrow, "Phylum"] = i
corr_spearman_Phylum_LPC.PC[nrow, "p.value"] = p
corr_spearman_Phylum_LPC.PC[nrow, "rho"] = rho
corr_spearman_Phylum_LPC.PC[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_LPC.PC[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_LPC.PC[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_LPC.PC[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_LPC.PC$p.adjusted <- p.adjust(corr_spearman_Phylum_LPC.PC$p.value, method = "BH", n = 35)
corr_spearman_Phylum_LPC.PC$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_LPC.PC$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_LPC.PC$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_LPC.PC$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_LPC.PC, file = '/Users/student05/Documents/serum lipids/phylum/LPC.PC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipids ratio', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipids ratio', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(0.04, -0.75),xlab= 'LPC/PC serum lipids ratio', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und PLots Korrelation zwischen CER/SM-Verhältnis und phylum-level
corr_map_phylum_CER.SM <- filter(phylum_LI, !is.na(CER.SM))
corr_spearman_Phylum_CER.SM <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_CER.SM, !is.na(i))
y = tmp[,i]
x = tmp$CER.SM
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$CER.SM
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$CER.SM
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_CER.SM)+1
corr_spearman_Phylum_CER.SM[nrow,"FA"] <- "CER.SM"
corr_spearman_Phylum_CER.SM[nrow, "Phylum"] = i
corr_spearman_Phylum_CER.SM[nrow, "p.value"] = p
corr_spearman_Phylum_CER.SM[nrow, "rho"] = rho
corr_spearman_Phylum_CER.SM[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_CER.SM[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_CER.SM[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_CER.SM[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_CER.SM$p.adjusted <- p.adjust(corr_spearman_Phylum_CER.SM$p.value, method = "BH", n = 35)
corr_spearman_Phylum_CER.SM$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_CER.SM$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_CER.SM$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_CER.SM$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_CER.SM, file = '/Users/student05/Documents/serum lipids/phylum/CER.SM.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0.02, -0.9), xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(0.03, -0.7),xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und PLots Korrelation zwischen HexCER/CER-Verhältnis und phylum-level
corr_map_phylum_HexCer.CER <- filter(phylum_LI, !is.na(HexCer.CER))
corr_spearman_Phylum_HexCer.CER <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_HexCer.CER, !is.na(i))
y = tmp[,i]
x = tmp$HexCer.CER
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$HexCer.CER
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$HexCer.CER
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_HexCer.CER)+1
corr_spearman_Phylum_HexCer.CER[nrow,"FA"] <- "HexCer.CER"
corr_spearman_Phylum_HexCer.CER[nrow, "Phylum"] = i
corr_spearman_Phylum_HexCer.CER[nrow, "p.value"] = p
corr_spearman_Phylum_HexCer.CER[nrow, "rho"] = rho
corr_spearman_Phylum_HexCer.CER[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_HexCer.CER[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_HexCer.CER[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_HexCer.CER[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_HexCer.CER$p.adjusted <- p.adjust(corr_spearman_Phylum_HexCer.CER$p.value, method = "BH", n = 35)
corr_spearman_Phylum_HexCer.CER$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_HexCer.CER$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_HexCer.CER$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_HexCer.CER$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_HexCer.CER, file = '/Users/student05/Documents/serum lipids/phylum/HexCer.CER.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und PLots Korrelation zwischen PC/PE-Verhältnis und phylum-level In Arbeit
corr_map_phylum_PC.PE <- filter(phylum_LI, !is.na(PC.PE))
corr_spearman_Phylum_PC.PE <- data.frame()
for( i in phylum_colnames) {
tmp <- filter(corr_map_phylum_PC.PE, !is.na(i))
y = tmp[,i]
x = tmp$PC.PE
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PC.PE
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PC.PE
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_Phylum_PC.PE)+1
corr_spearman_Phylum_PC.PE[nrow,"FA"] <- "PC.PE"
corr_spearman_Phylum_PC.PE[nrow, "Phylum"] = i
corr_spearman_Phylum_PC.PE[nrow, "p.value"] = p
corr_spearman_Phylum_PC.PE[nrow, "rho"] = rho
corr_spearman_Phylum_PC.PE[nrow, "p.value_PRE"] = p_PRE
corr_spearman_Phylum_PC.PE[nrow, "rho_PRE"] = rho_PRE
corr_spearman_Phylum_PC.PE[nrow, "p.value_POST"] = p_POST
corr_spearman_Phylum_PC.PE[nrow, "rho_POST"] = rho_POST
}
corr_spearman_Phylum_PC.PE$p.adjusted <- p.adjust(corr_spearman_Phylum_PC.PE$p.value, method = "BH", n = 35)
corr_spearman_Phylum_PC.PE$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PC.PE$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_PC.PE$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PC.PE$p.value_POST, method = "BH", n = 35)
write.table(corr_spearman_Phylum_PC.PE, file = '/Users/student05/Documents/serum lipids/phylum/PC.PE.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipids ratio',cor.coef.coord = c(30, -1.5), cor.coef.size = 6, ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
pdf("/Users/student05/Documents/fertige Plots/PC.PE.Proteo.pdf",width=8, height=10)
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('skyblue', 'orchid'), size = 2.5, add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(30, -1.7 ), cor.coef.size = 7,xlab= 'PC/PE Verhältnis', ylab = 'Relatives Vorkommen p__Proteobacteria [%]')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
scale_y_log10(labels = percent_format())+
theme(legend.position="none")
dev.off()
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipids ratio',cor.coef.coord = c(30, -1.4), cor.coef.size = 6, ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 6, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Korrelationsanalysen zwischen Genus-level und Serumlipiden
Subsetten des Genus-level, log-Transformation, hinzufügen von Pseudocount 0.0001 Filtern nach PRE und POST Proben
genus_colnames <- colnames(relab_genus_spread[, c(3:31)])
relab_genus_ID1 <- relab_genus_ID[,c(3:31)] + 0.00001
relab_genus_ID_log <- log10(relab_genus_ID_log)
genus_LI <- cbind(relab_genus_ID1, LI_serum)
genus_LI <- subset(filter(genus_LI, !Proband == '31KE'))
genus_LI <- subset(filter(genus_LI, !Proband == '45GL'))
genus_LI <- subset(filter(genus_LI, !Proband == '34WF'))
genus_LI <- subset(filter(genus_LI, !Proband == '54SL'))
genus_LI <- subset(filter(genus_LI, !Proband == '74SA'))
genus_LI$Time <- factor(genus_LI$Time, levels = c("PRE", "POST"))
Loop und Plots Korrelation zwischen Phosphatidylcholin und genus-level
corr_map_genus_PC <- filter(genus_LI, !is.na(PC))
corr_spearman_genus_PC <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_PC, !is.na(i))
y = tmp[,i]
x = tmp$PC
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PC
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PC
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_PC)+1
corr_spearman_genus_PC[nrow,"FA"] = "PC"
corr_spearman_genus_PC[nrow, "Genus"] = i
corr_spearman_genus_PC[nrow, "p.value"] = p
corr_spearman_genus_PC[nrow, "rho"] = rho
corr_spearman_genus_PC[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_PC[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_PC[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_PC[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_PC$p.adjusted <- p.adjust(corr_spearman_genus_PC$p.value, method = "BH", n = 35)
corr_spearman_genus_PC$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PC$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_PC$p.adjusted_POST <- p.adjust(corr_spearman_genus_PC$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_PC, file = '/Users/student05/Documents/serum lipids/genus/PC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', cor.coef.coord = c(800, -1.6), cor.coef.size = 6,ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', cor.coef.coord = c(800, -1.6), cor.coef.size = 6,ylab = 'log10 (Relative Abundance g__Coprococcus')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]',cor.coef.coord = c(800, -2), cor.coef.size = 6, ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]',cor.coef.coord = c(800, -2), cor.coef.size = 6, ylab = 'log10 (Relative Abundance g__Sutterella')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Phosphatidylcholin-Plasmalogen und genus-level
corr_map_genus_PCO <- filter(genus_LI, !is.na(PCO))
corr_spearman_genus_PCO <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_PCO, !is.na(i))
y = tmp[,i]
x = tmp$PCO
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PCO
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PCO
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_PCO)+1
corr_spearman_genus_PCO[nrow,"FA"] = "PCO"
corr_spearman_genus_PCO[nrow, "Genus"] = i
corr_spearman_genus_PCO[nrow, "p.value"] = p
corr_spearman_genus_PCO[nrow, "rho"] = rho
corr_spearman_genus_PCO[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_PCO[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_PCO[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_PCO[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_PCO$p.adjusted <- p.adjust(corr_spearman_genus_PCO$p.value, method = "BH", n = 35)
corr_spearman_genus_PCO$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PCO$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_PCO$p.adjusted_POST <- p.adjust(corr_spearman_genus_PCO$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_PCO, file = '/Users/student05/Documents/serum lipids/genus/PCO.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Sphingomyelin und genus-level
corr_map_genus_SM <- filter(genus_LI, !is.na(SM))
corr_spearman_genus_SM <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_SM, !is.na(i))
y = tmp[,i]
x = tmp$SM
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$SM
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$SM
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_SM)+1
corr_spearman_genus_SM[nrow,"FA"] = "SM"
corr_spearman_genus_SM[nrow, "Genus"] = i
corr_spearman_genus_SM[nrow, "p.value"] = p
corr_spearman_genus_SM[nrow, "rho"] = rho
corr_spearman_genus_SM[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_SM[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_SM[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_SM[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_SM$p.adjusted <- p.adjust(corr_spearman_genus_SM$p.value, method = "BH", n = 35)
corr_spearman_genus_SM$p.adjusted_PRE <- p.adjust(corr_spearman_genus_SM$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_SM$p.adjusted_POST <- p.adjust(corr_spearman_genus_SM$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_SM, file = '/Users/student05/Documents/serum lipids/genus/SM.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]',cor.coef.size = 6,cor.coef.coord = c(200, -1.5), ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]',cor.coef.size = 6,cor.coef.coord = c(200, -1.5), ylab = 'log10 (Relative Abundance g__Coprococcus')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Phosphatidylethanolamin und genus-level
corr_map_genus_PE <- filter(genus_LI, !is.na(PE))
corr_spearman_genus_PE <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_PE, !is.na(i))
y = tmp[,i]
x = tmp$PE
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PE
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PE
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_PE)+1
corr_spearman_genus_PE[nrow,"FA"] = "PE"
corr_spearman_genus_PE[nrow, "Genus"] = i
corr_spearman_genus_PE[nrow, "p.value"] = p
corr_spearman_genus_PE[nrow, "rho"] = rho
corr_spearman_genus_PE[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_PE[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_PE[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_PE[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_PE$p.adjusted <- p.adjust(corr_spearman_genus_PE$p.value, method = "BH", n = 35)
corr_spearman_genus_PE$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PE$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_PE$p.adjusted_POST <- p.adjust(corr_spearman_genus_PE$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_PE, file = '/Users/student05/Documents/serum lipids/genus/PE.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Phosphatidylinositol und genus-level
corr_map_genus_PI <- filter(genus_LI, !is.na(PI))
corr_spearman_genus_PI <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_PI, !is.na(i))
y = tmp[,i]
x = tmp$PI
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PI
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PI
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_PI)+1
corr_spearman_genus_PI[nrow,"FA"] = "PI"
corr_spearman_genus_PI[nrow, "Genus"] = i
corr_spearman_genus_PI[nrow, "p.value"] = p
corr_spearman_genus_PI[nrow, "rho"] = rho
corr_spearman_genus_PI[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_PI[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_PI[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_PI[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_PI$p.adjusted <- p.adjust(corr_spearman_genus_PI$p.value, method = "BH", n = 35)
corr_spearman_genus_PI$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PI$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_PI$p.adjusted_POST <- p.adjust(corr_spearman_genus_PI$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_PI, file = '/Users/student05/Documents/serum lipids/genus/PI.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Phosphatidylethanolamin-Plasmalogen und genus-level
corr_map_genus_PEP <- filter(genus_LI, !is.na(PEP))
corr_spearman_genus_PEP <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_PEP, !is.na(i))
y = tmp[,i]
x = tmp$PEP
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PEP
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PEP
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_PEP)+1
corr_spearman_genus_PEP[nrow,"FA"] = "PEP"
corr_spearman_genus_PEP[nrow, "Genus"] = i
corr_spearman_genus_PEP[nrow, "p.value"] = p
corr_spearman_genus_PEP[nrow, "rho"] = rho
corr_spearman_genus_PEP[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_PEP[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_PEP[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_PEP[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_PEP$p.adjusted <- p.adjust(corr_spearman_genus_PEP$p.value, method = "BH", n = 35)
corr_spearman_genus_PEP$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PEP$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_PEP$p.adjusted_POST <- p.adjust(corr_spearman_genus_PEP$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_PEP, file = '/Users/student05/Documents/serum lipids/genus/PEP.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Lysophosphatidylcholin und genus-level
corr_map_genus_LPC <- filter(genus_LI, !is.na(LPC))
corr_spearman_genus_LPC <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_LPC, !is.na(i))
y = tmp[,i]
x = tmp$LPC
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$LPC
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$LPC
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_LPC)+1
corr_spearman_genus_LPC[nrow,"FA"] = "LPC"
corr_spearman_genus_LPC[nrow, "Genus"] = i
corr_spearman_genus_LPC[nrow, "p.value"] = p
corr_spearman_genus_LPC[nrow, "rho"] = rho
corr_spearman_genus_LPC[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_LPC[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_LPC[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_LPC[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_LPC$p.adjusted <- p.adjust(corr_spearman_genus_LPC$p.value, method = "BH", n = 35)
corr_spearman_genus_LPC$p.adjusted_PRE <- p.adjust(corr_spearman_genus_LPC$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_LPC$p.adjusted_POST <- p.adjust(corr_spearman_genus_LPC$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_LPC, file = '/Users/student05/Documents/serum lipids/genus/LPC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Ceramid und genus-level
corr_map_genus_CER <- filter(genus_LI, !is.na(CER))
corr_spearman_genus_CER <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_CER, !is.na(i))
y = tmp[,i]
x = tmp$CER
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$CER
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$CER
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_CER)+1
corr_spearman_genus_CER[nrow,"FA"] = "CER"
corr_spearman_genus_CER[nrow, "Genus"] = i
corr_spearman_genus_CER[nrow, "p.value"] = p
corr_spearman_genus_CER[nrow, "rho"] = rho
corr_spearman_genus_CER[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_CER[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_CER[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_CER[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_CER$p.adjusted <- p.adjust(corr_spearman_genus_CER$p.value, method = "BH", n = 35)
corr_spearman_genus_CER$p.adjusted_PRE <- p.adjust(corr_spearman_genus_CER$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_CER$p.adjusted_POST <- p.adjust(corr_spearman_genus_CER$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_CER, file = '/Users/student05/Documents/serum lipids/genus/CER.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen Hexosylceramid und genus-level
corr_map_genus_HexCer <- filter(genus_LI, !is.na(HexCer))
corr_spearman_genus_HexCer <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_HexCer, !is.na(i))
y = tmp[,i]
x = tmp$HexCer
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$HexCer
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$HexCer
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_HexCer)+1
corr_spearman_genus_HexCer[nrow,"FA"] = "HexCer"
corr_spearman_genus_HexCer[nrow, "Genus"] = i
corr_spearman_genus_HexCer[nrow, "p.value"] = p
corr_spearman_genus_HexCer[nrow, "rho"] = rho
corr_spearman_genus_HexCer[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_HexCer[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_HexCer[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_HexCer[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_HexCer$p.adjusted <- p.adjust(corr_spearman_genus_HexCer$p.value, method = "BH", n = 35)
corr_spearman_genus_HexCer$p.adjusted_PRE <- p.adjust(corr_spearman_genus_HexCer$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_HexCer$p.adjusted_POST <- p.adjust(corr_spearman_genus_HexCer$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_HexCer, file = '/Users/student05/Documents/serum lipids/genus/HexCer.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, label= 'Proband',
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Prevotella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen summierten Serumlipiden und genus-level
corr_map_genus_Sum <- filter(genus_LI, !is.na(Sum))
corr_spearman_genus_Sum <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Sum, !is.na(i))
y = tmp[,i]
x = tmp$Sum
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Sum
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Sum
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Sum)+1
corr_spearman_genus_Sum[nrow,"FA"] = "Sum"
corr_spearman_genus_Sum[nrow, "Genus"] = i
corr_spearman_genus_Sum[nrow, "p.value"] = p
corr_spearman_genus_Sum[nrow, "rho"] = rho
corr_spearman_genus_Sum[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Sum[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Sum[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Sum[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Sum$p.adjusted <- p.adjust(corr_spearman_genus_Sum$p.value, method = "BH", n = 35)
corr_spearman_genus_Sum$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Sum$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Sum$p.adjusted_POST <- p.adjust(corr_spearman_genus_Sum$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_Sum, file = '/Users/student05/Documents/serum lipids/genus/Sum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen summierten Membranlipiden und genus-level
corr_map_genus_Sum.Membrane <- filter(genus_LI, !is.na(Sum.Membrane))
corr_spearman_genus_Sum.Membrane <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Sum.Membrane, !is.na(i))
y = tmp[,i]
x = tmp$Sum.Membrane
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Sum.Membrane
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Sum.Membrane
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Sum.Membrane)+1
corr_spearman_genus_Sum.Membrane[nrow,"FA"] = "Sum.Membrane"
corr_spearman_genus_Sum.Membrane[nrow, "Genus"] = i
corr_spearman_genus_Sum.Membrane[nrow, "p.value"] = p
corr_spearman_genus_Sum.Membrane[nrow, "rho"] = rho
corr_spearman_genus_Sum.Membrane[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Sum.Membrane[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Sum.Membrane[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Sum.Membrane[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Sum.Membrane$p.adjusted <- p.adjust(corr_spearman_genus_Sum.Membrane$p.value, method = "BH", n = 35)
corr_spearman_genus_Sum.Membrane$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Sum.Membrane$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Sum.Membrane$p.adjusted_POST <- p.adjust(corr_spearman_genus_Sum.Membrane$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_Sum.Membrane, file = '/Users/student05/Documents/serum lipids/genus/Sum.Membrane.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen summierten Storagelipiden und genus-level
corr_map_genus_Sum.Storage <- filter(genus_LI, !is.na(Sum.Storage))
corr_spearman_genus_Sum.Storage <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Sum.Storage, !is.na(i))
y = tmp[,i]
x = tmp$Sum.Storage
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Sum.Storage
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Sum.Storage
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Sum.Storage)+1
corr_spearman_genus_Sum.Storage[nrow,"FA"] = "Sum.Storage"
corr_spearman_genus_Sum.Storage[nrow, "Genus"] = i
corr_spearman_genus_Sum.Storage[nrow, "p.value"] = p
corr_spearman_genus_Sum.Storage[nrow, "rho"] = rho
corr_spearman_genus_Sum.Storage[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Sum.Storage[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Sum.Storage[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Sum.Storage[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Sum.Storage$p.adjusted <- p.adjust(corr_spearman_genus_Sum.Storage$p.value, method = "BH", n = 35)
corr_spearman_genus_Sum.Storage$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Sum.Storage$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Sum.Storage$p.adjusted_POST <- p.adjust(corr_spearman_genus_Sum.Storage$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_Sum.Storage, file = '/Users/student05/Documents/serum lipids/genus/Sum.Storage.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen summierten Lysolipiden und genus-level
corr_map_genus_Sum.Lyso <- filter(genus_LI, !is.na(Sum.Lyso))
corr_spearman_genus_Sum.Lyso <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_Sum.Lyso, !is.na(i))
y = tmp[,i]
x = tmp$Sum.Lyso
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$Sum.Lyso
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$Sum.Lyso
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_Sum.Lyso)+1
corr_spearman_genus_Sum.Lyso[nrow,"FA"] = "Sum.Lyso"
corr_spearman_genus_Sum.Lyso[nrow, "Genus"] = i
corr_spearman_genus_Sum.Lyso[nrow, "p.value"] = p
corr_spearman_genus_Sum.Lyso[nrow, "rho"] = rho
corr_spearman_genus_Sum.Lyso[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_Sum.Lyso[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_Sum.Lyso[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_Sum.Lyso[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_Sum.Lyso$p.adjusted <- p.adjust(corr_spearman_genus_Sum.Lyso$p.value, method = "BH", n = 35)
corr_spearman_genus_Sum.Lyso$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Sum.Lyso$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_Sum.Lyso$p.adjusted_POST <- p.adjust(corr_spearman_genus_Sum.Lyso$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_Sum.Lyso, file = '/Users/student05/Documents/serum lipids/genus/Sum.Lyso.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -1.5), xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen LPC/PE-Verhältnis und genus-level
corr_map_genus_LPC.PC <- filter(genus_LI, !is.na(LPC.PC))
corr_spearman_genus_LPC.PC <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_LPC.PC, !is.na(i))
y = tmp[,i]
x = tmp$LPC.PC
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$LPC.PC
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$LPC.PC
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_LPC.PC)+1
corr_spearman_genus_LPC.PC[nrow,"FA"] = "LPC.PC"
corr_spearman_genus_LPC.PC[nrow, "Genus"] = i
corr_spearman_genus_LPC.PC[nrow, "p.value"] = p
corr_spearman_genus_LPC.PC[nrow, "rho"] = rho
corr_spearman_genus_LPC.PC[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_LPC.PC[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_LPC.PC[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_LPC.PC[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_LPC.PC$p.adjusted <- p.adjust(corr_spearman_genus_LPC.PC$p.value, method = "BH", n = 35)
corr_spearman_genus_LPC.PC$p.adjusted_PRE <- p.adjust(corr_spearman_genus_LPC.PC$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_LPC.PC$p.adjusted_POST <- p.adjust(corr_spearman_genus_LPC.PC$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_LPC.PC, file = '/Users/student05/Documents/serum lipids/genus/LPC.PC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipid ratio', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0.03, -1), xlab= 'LPC/PC serum lipid ratio', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Porphyromonadaceae.g__Parabacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipid ratio', ylab = 'log10 (Relative Abundance g__Parabacteroides')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipid ratio', ylab = 'log10 (Relative Abundance g__Lachnospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen CER/SM-Verhältnis und genus-level
corr_map_genus_CER.SM <- filter(genus_LI, !is.na(CER.SM))
corr_spearman_genus_CER.SM <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_CER.SM, !is.na(i))
y = tmp[,i]
x = tmp$CER.SM
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$CER.SM
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$CER.SM
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_CER.SM)+1
corr_spearman_genus_CER.SM[nrow,"FA"] = "CER.SM"
corr_spearman_genus_CER.SM[nrow, "Genus"] = i
corr_spearman_genus_CER.SM[nrow, "p.value"] = p
corr_spearman_genus_CER.SM[nrow, "rho"] = rho
corr_spearman_genus_CER.SM[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_CER.SM[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_CER.SM[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_CER.SM[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_CER.SM$p.adjusted <- p.adjust(corr_spearman_genus_CER.SM$p.value, method = "BH", n = 35)
corr_spearman_genus_CER.SM$p.adjusted_PRE <- p.adjust(corr_spearman_genus_CER.SM$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_CER.SM$p.adjusted_POST <- p.adjust(corr_spearman_genus_CER.SM$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_CER.SM, file = '/Users/student05/Documents/serum lipids/genus/CER.SM.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid ratio', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Clostridiaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen HexCer/CER-Verhältnis und genus-level
corr_map_genus_HexCer.CER <- filter(genus_LI, !is.na(HexCer.CER))
corr_spearman_genus_HexCer.CER <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_HexCer.CER, !is.na(i))
y = tmp[,i]
x = tmp$HexCer.CER
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$HexCer.CER
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$HexCer.CER
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_HexCer.CER)+1
corr_spearman_genus_HexCer.CER[nrow,"FA"] = "HexCer.CER"
corr_spearman_genus_HexCer.CER[nrow, "Genus"] = i
corr_spearman_genus_HexCer.CER[nrow, "p.value"] = p
corr_spearman_genus_HexCer.CER[nrow, "rho"] = rho
corr_spearman_genus_HexCer.CER[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_HexCer.CER[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_HexCer.CER[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_HexCer.CER[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_HexCer.CER$p.adjusted <- p.adjust(corr_spearman_genus_HexCer.CER$p.value, method = "BH", n = 35)
corr_spearman_genus_HexCer.CER$p.adjusted_PRE <- p.adjust(corr_spearman_genus_HexCer.CER$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_HexCer.CER$p.adjusted_POST <- p.adjust(corr_spearman_genus_HexCer.CER$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_HexCer.CER, file = '/Users/student05/Documents/serum lipids/genus/HexCer.CER.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid ratio', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
Loop und Plots Korrelation zwischen PC/PE-Verhältnis und genus-level
In Arbeit
corr_map_genus_PC.PE <- filter(genus_LI, !is.na(PC.PE))
corr_spearman_genus_PC.PE <- data.frame()
for( i in genus_colnames) {
tmp <- filter(corr_map_genus_PC.PE, !is.na(i))
y = tmp[,i]
x = tmp$PC.PE
tmp_corr_spearman <- cor.test(x, y, method="spearman")
rho = tmp_corr_spearman$estimate
p = tmp_corr_spearman$p.value
z = subset(filter(tmp, Time == "PRE"))[,i]
w = subset(filter(tmp, Time == "PRE"))$PC.PE
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
rho_PRE = tmp_corr_spearman_PRE$estimate
p_PRE = tmp_corr_spearman_PRE$p.value
r = subset(filter(tmp, Time == "POST"))[,i]
s = subset(filter(tmp, Time == "POST"))$PC.PE
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
rho_POST = tmp_corr_spearman_POST$estimate
p_POST = tmp_corr_spearman_POST$p.value
nrow = nrow(corr_spearman_genus_PC.PE)+1
corr_spearman_genus_PC.PE[nrow,"FA"] = "PC.PE"
corr_spearman_genus_PC.PE[nrow, "Genus"] = i
corr_spearman_genus_PC.PE[nrow, "p.value"] = p
corr_spearman_genus_PC.PE[nrow, "rho"] = rho
corr_spearman_genus_PC.PE[nrow, "p.value_PRE"] = p_PRE
corr_spearman_genus_PC.PE[nrow, "rho_PRE"] = rho_PRE
corr_spearman_genus_PC.PE[nrow, "p.value_POST"] = p_POST
corr_spearman_genus_PC.PE[nrow, "rho_POST"] = rho_POST
}
corr_spearman_genus_PC.PE$p.adjusted <- p.adjust(corr_spearman_genus_PC.PE$p.value, method = "BH", n = 35)
corr_spearman_genus_PC.PE$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PC.PE$p.value_PRE, method = "BH", n = 35)
corr_spearman_genus_PC.PE$p.adjusted_POST <- p.adjust(corr_spearman_genus_PC.PE$p.value_POST, method = "BH", n = 35)
write.table( corr_spearman_genus_PC.PE, file = '/Users/student05/Documents/serum lipids/genus/PC.PE.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid ratio', ylab = 'log10 (Relative Abundance g__Oscillospira')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line',conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text( hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid ratio', ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(hjust=1))+
theme(legend.position="none")
pdf("/Users/student05/Documents/fertige Plots/PC.PE.Proteo.pdf",width=8, height=10)
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(30, -1.7 ), cor.coef.size = 7,xlab= 'PC/PE Verhältnis', ylab = 'Relatives Vorkommen p__Proteobacteria [%]')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
scale_y_log10(labels = percent_format())+
theme(legend.position="none")
dev.off()
genus_LI$Time <- factor(genus_LI$Time, levels = c("PRE", "POST"))
pdf("/Users/student05/Documents/fertige Plots/PC.PE.bacteroides.pdf",width=8, height=10)
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',size = 2.5,color = 'Time', palette = c('skyblue', 'orchid'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(30, -1), cor.coef.size = 7,xlab= 'PC/PE Verhältnis', ylab = 'Relatives Vorkommen g__Bacteroides [%]')+
facet_grid(.~ Time, scales = "free_x")+
theme(strip.text.x = element_text(size = 18, colour = "black"))+
theme(text = element_text(size=18),
axis.text.x = element_text(hjust=1))+
scale_y_log10(labels = percent_format())+
theme(legend.position="none")
dev.off()
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid concentration [nmol/ml]',cor.coef.coord = c(30, -1.1), cor.coef.size = 6, ylab = 'log10 (Relative Abundance g__Bacteroides')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid concentration [nmol/ml]',cor.coef.coord = c(30, -1.1), cor.coef.size = 6, ylab = 'log10 (Relative Abundance g__Bacteroides')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
theme(text = element_text(size=15),
axis.text.x = element_text(angle=0, hjust=1))+
theme(legend.position="none")
ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE,
cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
facet_grid(.~ Time,scales = "free_x")+
theme(strip.text.x = element_text(size = 10, colour = "black"))+
theme(text = element_text(size=13),
axis.text.x = element_text(angle=60, hjust=1))+
theme(legend.position="none")
---
title: "Bachelorarbeit Lena SS2019"
output:
  html_notebook: default
  html_document:
    df_print: paged
  pdf_document: default
---

Verwendete Pakete laden und installieren

```{r}
install.packages("vegan")
install.packages("dpylr")
install.packages("ggplot2")
install.packages("nortest")
install.packages("ggpubr")
install.packages("cowplot")
install.packages("ggsignif")
install.packages("tidyverse")
install.packages("Hmisc")
install.packages("corrplot")
install.packages("PerformanceAnalytics")
install.packages("xts")
install.packages("quadprog")
install.packages("Rmisc")

library("dplyr")
library("vegan")
library("ggplot2")
library("nortest")
library("biomformat")
library("ggpubr")
library("cowplot")
library("ggsignif")
library("reshape2")
library("tidyverse")
library("Hmisc")
library("corrplot")
library("PerformanceAnalytics")
library("xts")
```

1.SCFA Analyse 
1.1 Normalverteilung

```{r}
SCFA_stool <- read.table("/Users/student05/Downloads/SCFA_stool total SCFA.txt", sep = '\t', comment='',head=T, row.names = 1)

View(SCFA_stool)

SCFA_stool<- add.rownames(SCFA_stool, "SampleID")

SCFA_stool$Time <-factor(SCFA_stool$Time, levels = c("PRE", "POST", "FOLLOWUP"))

SCFA_stool[1,3]<- "PRE"

SCFA_stool[1,4]<- "OU1"

scfa_colnames <- colnames(SCFA_stool[, c(6:10)])

nd.SCFA<- data_frame()
scfa
scfa_colnames[1]

for (i in scfa_colnames)  {
  fit <- shapiro.test(as.matrix(as.data.frame(lapply(SCFA_stool[,i],
                                                     as.numeric))))
  p = fit$p.value
  nrow = nrow(nd.SCFA)+1
  nd.SCFA[nrow, "column"] = i
  nd.SCFA[nrow, "p.value"] = round(p, 4)
}

 sign.nd_SCFA <- filter(nd.SCFA, p > 0.05)
 
ggqqplot(SCFA_stool$Acetate, ylab = "Acetate concentration nmol/mg", xlab = "SampleID")
ggqqplot(SCFA_stool$Iso.Butyrate, ylab = "Iso-Butyrate concentration nmol/mg", xlab = "SampleID")
ggqqplot(SCFA_stool$Propionate, ylab = "Propionate concentration nmol/mg", xlab = "SampleID")
ggqqplot(SCFA_stool$Butyrate, ylab = "Butyrate concentration nmol/mg", xlab = "SampleID")
 
```

Filtern der SCFA-Daten nach PRE und POST Proben

```{r}
SCFA_stool_pairs <- filter(SCFA_stool, Proband == "05AP" | Proband == "06WT"
                              
                              | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                              
                              | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                              
                              | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                              
                              | Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
                              
                              | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                              
                              | Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
                              
                              | Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
                              
                              | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")




SCFA_stool_pairs_PP <- filter(SCFA_stool_pairs, Time=="PRE" | Time=="POST")



SCFA_stool_pairs_PPFU <- filter(SCFA_stool, Proband == "05AP" | Proband == "13
                                 
                                 BS" | Proband == "17SK" | Proband == "22WS" | Proband ==
                                   
                                   "40WA" | Proband == "41ML" | Proband == "54SL")

```

Wilcoxon-Test zwischen den Zeitpunkten der SCFA

PRE und POST
```{r}
wilcox_SCFA<- data_frame()

for (i in scfa_colnames) {
  
tmp<- filter(SCFA_stool_pairs_PP[ ,i])
  
  x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
  
  y <- SCFA_stool_pairs_PP$Time 
  
  tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = TRUE)
  
  p <- tmp_wilcox$p.value
  
  nrow = nrow(wilcox_SCFA)+1
  
  wilcox_SCFA[nrow, "SCFA"] <- i

  wilcox_SCFA[nrow, "Mean PRE"] <- round(apply(subset(filter(SCFA_stool_pairs, Time == "PRE")[,i], na.rm = TRUE), 2, mean, na.rm = TRUE), 4)
  
  wilcox_SCFA[nrow, "sd PRE"] <- round(apply(subset(filter(SCFA_stool_pairs,Time == "PRE")[,i], na.rm = TRUE), 2, sd,na.rm = TRUE), 4)
  
  wilcox_SCFA[nrow, "Mean POST"] <-round(apply(subset(filter(SCFA_stool_pairs,Time == "POST")[,i], na.rm = TRUE), 2, mean,na.rm = TRUE), 4)
  
  wilcox_SCFA[nrow, "sd POST"] <- round(apply(subset(filter(SCFA_stool_pairs,Time == "POST")[,i], na.rm = TRUE), 2, sd,na.rm = TRUE), 4)
  
  wilcox_SCFA[nrow, "p.value"] <- round(p, 4) }

```
Acetate p.value = 0.025 -> signifikanter Unterschied!, mean PRE = 205.3, sd PRE = 148.3, mean POST = 132.58, sd POST = 79
Propionate p=0.136 -> kein signifikanter Unterschied!, mean PRE = 78.4, sd PRE = 62.7, mean POST = 54.3, sd POST = 33.1 
Butyrate p-value = 0.346 -> kein signifikanter Unterschied!, mean PRE = 59.2, sd PRE = 41.4, mean POST = 44.5, sd POST = 27 
Isobutyrate p-value = 0.571 -> kein signifikanter Unterschied!, mean PRE = 9.39, sd PRE = 4.55, mean POST = 9.17, sd POST = 3.1


Wilcoxon-Test Follow-up

```{r}
wilcox_SCFA_FU <- data_frame()
       
 for (i in scfa_colnames) {
   
   tmp <- filter(SCFA_stool_pairs_PPFU[,i], !is.na(i))
   
   x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
   
   y <- SCFA_stool_pairs_PPFU$Time
   
   tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = TRUE)
   
   p_POST_FU <- tmp_wilcox$p.value[1]
   
   p_PRE_FU <- tmp_wilcox$p.value[2]
   
   p_PRE_POST <- tmp_wilcox$p.value[4]
   
   nrow = nrow(wilcox_SCFA_FU)+1
   
   wilcox_SCFA_FU[nrow, "SCFA"] <- i
   
   wilcox_SCFA_FU[nrow, "Mean FOLLOWUP"] <- round(apply(subset(filter(SCFA_stool_pairs_PPFU, Time == "FOLLOWUP")[,i], na.rm = TRUE),2, mean, na.rm = TRUE), 4)
   
  wilcox_SCFA_FU[nrow, "sd FOLLOWUP"] <- round(apply(subset(filter(SCFA_stool_pairs_PPFU, Time =="FOLLOWUP")[,i], na.rm = TRUE), 2, sd, na.rm = TRUE), 4)
   
   wilcox_SCFA_FU[nrow, "p.value_POST_FU"] <- round(p_POST_FU, 2)
   
   wilcox_SCFA_FU[nrow, "p.value_PRE_FU"] <- round(p_PRE_FU, 2)
   
   wilcox_SCFA_FU[nrow, "p.value_PRE_POST"] <- round(p_PRE_POST, 2)
 }
```
Acetate p.valuePOST/FU = 0.56, p.valuePRE/FU = 0.47, p.valuePRE/POST = 0.47 -> alles kein signifikanter Unterschied! mean FU = 173, sd FU = 43.7 
Propionate p.valuePOST/FU = 0.94, p.valuePRE/FU = 0.94, p.valuePRE/POST = 0.94 -> alles kein signifnikanter Unterschied! mean FU = 58.7, sd FU= 10.1
Butyrate p.valuePOST/FU = 0.7, p.valuePRE/FU = 0.7, p.valuePRE/POST = 0.7 -> kein signifikanter Unterschied bei allen! mean FU = 43.5, sd FU = 17.9
Isobutyrate p.valuePOST/FU = 1, p.valuePRE/POST = 1, p.valuePRE/FU = 1 -> bei allen kein signifikanter Unterschied! mean FU = 9.14, sd FU = 2.13


Plotten aller SCFA 
Alle Zeiten zusammen

```{r}

SCFA_stool.melt <- melt(SCFA_stool_pairs, id.vars = 'Time', measure.vars = c('Acetate', 'Propionate', 'Butyrate', 'Iso.Butyrate'))
SCFA_stool.melt <- subset(filter(SCFA_stool.melt, !Time == 'FOLLOWUP'))
SCFA_stool.melt <- dplyr::rename(SCFA_stool.melt, SCFA=variable)
SCFA_stool.melt <- dplyr::rename(SCFA_stool.melt, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/SCFA.alltimes.neu.pdf",width=6, height=10)
ggplot(SCFA_stool.melt,aes(x=SCFA, y=Concentration, fill= SCFA)) +
  xlab ('SCFA') + ylab ('Konzentrationen [µmol/g]') + 
  geom_boxplot(width = .4, lwd=1)  + theme_classic()+
  scale_fill_manual(labels = c("Acetate", "Propionate", "Butyrate", "Iso-Butyrate"), values = c("seagreen4", "seagreen3", "seagreen2", "seagreen1"))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
dev.off()

```

Plotten der SCFA je Zeitpunkt
 
```{r}

pdf("/Users/student05/Documents/fertige Plots/SCFA.times.pdf",width=6, height=10)
ggplot(SCFA_stool.melt,aes(x=Time, y=Concentration, fill= SCFA)) +
  xlab ('Zeitpunkt') + ylab ('Konzentrationen [µmol/g]') + 
  geom_boxplot(width = 0.8, lwd=1) + 
  scale_fill_manual(labels = c("Acetate", "Propionate", "Butyrate", "Iso-Butyrate"), values = c("seagreen4", "seagreen3", "seagreen2", "seagreen1"))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text( hjust=1))+
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(legend.position="top")
dev.off()
```
 
Plotten der einzelnen SCFA
Acetat

```{r}
Acetate_stool.melt <- melt(SCFA_stool_pairs, id.vars = 'Time', measure.vars = c('Acetate'))
Acetate_stool.melt <- rename(Acetate_stool.melt, SCFA=variable)
Acetate_stool.melt <- rename(Acetate_stool.melt, Concentration=value)

ggplot(Acetate_stool.melt) +
  xlab ('Time Point') + ylab ('Concentration [mg/ml]') +
  geom_boxplot(aes(x=Time, y=Concentration, fill=SCFA)) +
  scale_fill_manual(labels = c("Acetate"), values = c("tomato")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons = list(c("PRE","POST")))

```

Boxplot mit je 2 SCFA je Zeitpunkt, linked by Probands

Acetat-Propionat

```{r}
SCFA_stool_melt_AP <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Acetate', 'Propionate'))
SCFA_stool_melt_AP <- rename(SCFA_stool_melt_AP, Concentration=value)
SCFA_stool_melt_AP <- rename(SCFA_stool_melt_AP, SCFA=variable)

ggpaired(SCFA_stool_melt_AP, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
         palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') + 
  xlab('SCFA') + ylab('Concentration [mg/ml]') +
scale_fill_manual(labels=c("Acetate", "Propionat"), values = c("yellowgreen", "steelblue2"))
```

Acetat-Butyrat

```{r}
SCFA_stool_melt_AB <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Acetate', 'Butyrate'))
SCFA_stool_melt_AB <- rename(SCFA_stool_melt_AB, Concentration=value)
SCFA_stool_melt_AB <- rename(SCFA_stool_melt_AB, SCFA=variable)


ggpaired(SCFA_stool_melt_AB, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
         palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') + 
  xlab('SCFA') + ylab('Concentration [mg/ml]') +
  scale_fill_manual(labels=c("Acetate", "Butyrate"), values = c("yellowgreen", "coral2"))
```

Acetat-Isobutyrat

```{r}
SCFA_stool_melt_AI <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Acetate', 'Iso.Butyrate'))
SCFA_stool_melt_AI <- rename(SCFA_stool_melt_AI, Concentration=value)
SCFA_stool_melt_AI <- rename(SCFA_stool_melt_AI, SCFA=variable)

ggpaired(SCFA_stool_melt_AI, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
         palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') + 
  xlab('SCFA') + ylab('Concentration [mg/ml]')+
  scale_fill_manual(labels=c("Acetate", "Iso.Butyrate"), values = c("yellowgreen", "deeppink"))
```

Propionat-Butyrat

```{r}
SCFA_stool_melt_PB <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Propionate', 'Butyrate'))
SCFA_stool_melt_PB <- rename(SCFA_stool_melt_PB, Concentration=value)
SCFA_stool_melt_PB <- rename(SCFA_stool_melt_PB, SCFA=variable)

ggpaired(SCFA_stool_melt_PB, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
         palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') + 
  xlab('SCFA') + ylab('Concentration [mg/ml]')+
  scale_fill_manual(labels=c("Propionate", "Butyrate"), values = c("steelblue2", "coral2"))
```

Butyrat-Isobutyrat

```{r}
SCFA_stool_melt_BI <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Butyrate', 'Iso.Butyrate'))
SCFA_stool_melt_BI <- rename(SCFA_stool_melt_BI, Concentration=value)
SCFA_stool_melt_BI <- rename(SCFA_stool_melt_BI, SCFA=variable)

ggpaired(SCFA_stool_melt_BI, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
         palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') + 
  xlab('SCFA') + ylab('Concentration [mg/ml]')+
  scale_fill_manual(labels=c("Butyrate", "Iso.Butyrate"), values = c("coral2", "deeppink"))
```

Propionat-Isobutyrat

```{r}
SCFA_stool_melt_PI <-melt(SCFA_stool_pairs, id.vars = c('Time','Proband'), measure.vars = c('Propionate','Iso.Butyrate'))
SCFA_stool_melt_PI <- rename(SCFA_stool_melt_PI, Concentration=value)
SCFA_stool_melt_PI <- rename(SCFA_stool_melt_PI, SCFA=variable)

 
ggpaired(SCFA_stool_melt_PI, x='SCFA', y='Concentration', color = 'black', fill= 'SCFA',
         palette = c('whitesmoke', 'whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE, label = 'Proband') + 
  xlab('SCFA') + ylab('Concentration [mg/ml]')+
  scale_fill_manual(labels=c("Propionate", "Iso.Butyrate"), values = c("steelblue2", "deeppink"))
```

1.2 Erstellen einer Korrelationsmatrix zum Testen von Korrelationen zwischen den SCFA

```{r}
SCFA_stool <- read.table("/Users/student05/Downloads/SCFA_stool total SCFA.txt", sep = '\t', comment='',
                         head=T, row.names = 1)

write.table(SCFA_stool, file ='/Users/student05/Documents/SCFA/SCFA_stool total SCFA.txt',sep ="\t", col.names = TRUE, row.names = FALSE)


View(SCFA_stool)

SCFA_stool<- add_rownames(SCFA_stool, "SampleID")

SCFA_stool$Time <-factor(SCFA_stool$Time, levels = c("PRE", "POST", "FOLLOWUP"))

SCFA_stool_matrix_PRE <- subset(filter(SCFA_stool, Time == "PRE"))[ ,6:10]
SCFA_stool_matrix_POST <- subset(filter(SCFA_stool, Time == "POST"))[ ,6:10]

res.PRE <- cor(SCFA_stool_matrix_PRE)
res.POST <- cor(SCFA_stool_matrix_POST)
```

Spearman-Korrelation 

```{r}
res2.PRE <- rcorr(as.matrix(SCFA_stool_matrix_PRE), type = "spearman")

res2.POST <- rcorr(as.matrix(SCFA_stool_matrix_POST), type = "spearman")
```

Korrelationskoeffizient bestimmen

```{r}
res2.PRE$r
res2.POST$r

SCFA_stool_PRE_CC <- as.matrix((res2.PRE$r))
SCFA_stool_POST_CC <- as.matrix(res2.POST$r)
```

p-values bestimmen

```{r}
res2$P

SCFA_stool_PRE_PV <- as.matrix(res2.PRE$P)
SCFA_stool_POST_PV <- as.matrix(res2.POST$P)
```

flattenCorrMatrix erstellen für PRE und POST

```{r}

flattenCorrMatrix.PRE <- function(SCFA_stool_PRE_CC, SCFA_stool_PRE_PV) {
  ut <- upper.tri(SCFA_stool_PRE_CC)
  data.frame(
   row = rownames(SCFA_stool_PRE_CC)[row(SCFA_stool_PRE_CC)[ut]],
   column = rownames(SCFA_stool_PRE_CC)[col(SCFA_stool_PRE_CC)[ut]],
    cor  =(SCFA_stool_PRE_CC)[ut],
    p = SCFA_stool_PRE_PV[ut]
  )
}

flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P)


flattenCorrMatrix.POST <- function(SCFA_stool_POST_CC, SCFA_stool_POST_PV) {
  ut <- upper.tri(SCFA_stool_POST_CC)
  data.frame(
    row = rownames(SCFA_stool_POST_CC)[row(SCFA_stool_POST_CC)[ut]],
    column = rownames(SCFA_stool_POST_CC)[col(SCFA_stool_POST_CC)[ut]],
    cor  =(SCFA_stool_POST_CC)[ut],
    p = SCFA_stool_POST_PV[ut]
  )
}

flattenCorrMatrix.POST(res2.POST$r, res2.POST$P)
```

Dataframe erstellen

```{r}
SCFA_PRE_cor.p <- as.data.frame(flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P))
SCFA_POST_cor.p <- as.data.frame(flattenCorrMatrix.POST(res2.POST$r, res2.POST$P))


colnames(SCFA_PRE_cor.p) <- c("SCFA", "SCFA", "correlation coefficient", "p-value")
colnames(SCFA_POST_cor.p) <- c("SCFA", "SCFA", "correlation coefficient", "p-value")
```

Correlogram erstellen (Package corrplot)

```{r}
corrplot(res.PRE, type = "upper", order = "hclust", 
         tl.col = "black", tl.srt = 45)

corrplot(res.POST, type = "upper", order = "hclust", 
         tl.col = "black", tl.srt = 45)


corrplot(res2.PRE$r, type="upper", order="hclust", 
         p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")

corrplot(res2.PRE$r, type="upper", order="hclust", 
         p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
```

Scatter plots erstellen

```{r}
chart.Correlation(SCFA_stool_matrix_PRE, histogram=TRUE, pch=19)
chart.Correlation(SCFA_stool_matrix_POST, histogram = T, pch = 19)
```

heatmap erstellen

```{r}
col<- colorRampPalette(c("blue", "white", "red"))(20)
heatmap(x = res.PRE, col = col, symm = TRUE)
```

1.3 Korrelationen zwischen SCFA und Ballaststoffaufnahme 

```{r}
SCFA_stool.f <- read.table("/Users/student05/Documents/SCFA/scfa.fibre.txt", sep = '\t', comment='',head=T, row.names = 1)

SCFA_stool.f <- subset(filter(SCFA_stool.f, !Proband == '33MP'))

SCFA_stool.f[1,3]<- "PRE"

SCFA_stool.f$Time <-factor(SCFA_stool.f$Time, levels = c("PRE", "POST"))
```

Korrelationen zwischen allen SCFA und Ballaststoffaufnahme 
In Arbeit

```{r}
pdf("/Users/student05/Documents/fertige Plots/SCFA.Ballaststoffe.pdf",width=8, height=10)
ggscatter(SCFA_stool.f, x='Total.SCFA', y='Fibre', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', color = "grey59",fill = "lightgray",conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, 80),cor.coef.size = 8, xlab= 'Gesamt-SCFA Konzentrationen [µmol/g]', ylab = 'Ballaststoffaufnahme [g]')+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  geom_point(color='black', size=2.5)+
  theme(legend.position="none")
dev.off()
```

cortest einzelne SCFA

```{r}
cor.test(subset(filter(SCFA_stool.f))$Total.SCFA, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
cor.test(subset(filter(SCFA_stool.f))$Acetate, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
cor.test(subset(filter(SCFA_stool.f))$Propionate, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
cor.test(subset(filter(SCFA_stool.f))$Butyrate, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)
cor.test(subset(filter(SCFA_stool.f))$Iso.Butyrate, subset(filter(SCFA_stool.f))$Fibre, method = "spearman", exact = F)

p.adjust(c(0.1407,0.1844,0.2612, 0.06335, 0.986 ), method = 'BH', n=5)

```

Plotten Acetat-Balasststoffaufnahme

```{r}
ggscatter(SCFA_stool.f, x='Acetate', y='Fibre',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Acetate concentration [nmol/g]', ylab = 'Fiber intake [g]')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 
```

1.4 High und Low Sterolkonvertierungstypen SCFA

Nach Konvertierungstypen filtern und PRE und POST Proben zusammenfuegen

```{r}
lowconv <- filter(SCFA_stool, Proband == "05AP" | Proband == "33MP"
                      
                      | Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
                      
                      | Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")

lowconv['Phenotype'] = 'low converter'

highconv <- filter(SCFA_stool, Proband == "06WT" | Proband == "07RW"
                       
                       | Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
                       
                       | Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
                       
                       | Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
                       
                       | Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
                       
                       | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")

highconv['Phenotype'] = 'high converter'

highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL

noconv <- filter(SCFA_stool, Proband == "28HM" | Proband == "32FG"
                     
                     | Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
                     
                     | Proband == "39DA" | Proband == "66DG" | Proband == "70PL")

noconv['Phenotype'] = 'not classified'

noconv$Converter.Type <- NULL


convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)

convT_paired <- filter(convT, Proband == "05AP" | Proband == "06WT"
                                   
                                   | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                                   
                                   | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                                   
                                   | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                                   
                                   | Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
                                   
                                   | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                                   
                                   | Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
                                   
                                   | Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
                                   
                                   | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")


convT_paired_PP <- filter(convT_paired, Time=="PRE" | Time=="POST")
```

Boxplot SCFA je Sterolkonvertierungstyp

alle SCFA

```{r}
SCFA_stool.melt.CT <- melt(convT, id.vars = 'Phenotype', measure.vars = c('Acetate', 'Propionate', 'Butyrate', 'Iso.Butyrate'))
SCFA_stool.melt.CT <- rename(SCFA_stool.melt.CT, SCFA=variable)
SCFA_stool.melt.CT <- rename(SCFA_stool.melt.CT, Concentration=value)

ggplot(SCFA_stool.melt.CT,aes(x=Phenotype, y=Concentration, fill= SCFA)) +
  xlab ('Converter type') + ylab ('Concentration [mg/ml]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("Acetate", "Propionate", "Butyrate", "Iso-Butyrate"), values = c("tomato", "yellowgreen", "steelblue2", "orchid2"))
  
ggplot(subset(filter(convT, !Phenotype == "not classified")), aes(x=Phenotype, y=Acetate)) +
  xlab ('Phenotype') + ylab('Acetate Concentration [mg/ml]')+
  geom_boxplot(fill = 'whitesmoke', color = 'black')+
  geom_dotplot(bonaxis = 'y', stackdir = 'center', dotsize = 0.3, fill= 'grey22')

```

Acetat

```{r}
Acetate_stool.melt.CT <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('Acetate'))
Acetate_stool.melt.CT <- rename(Acetate_stool.melt.CT, SCFA=variable)
Acetate_stool.melt.CT <- rename(Acetate_stool.melt.CT, Concentration=value)

ggplot(filter(Acetate_stool.melt.CT, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [mg/ml]') + 
  scale_fill_manual(labels=c("Acetate"), values = c("yellowgreen"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))
     
```

Propionat

```{r}
Propionate_stool.melt.CT <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('Propionate'))
Propionate_stool.melt.CT <- rename(Propionate_stool.melt.CT, SCFA=variable)
Propionate_stool.melt.CT <- rename(Propionate_stool.melt.CT, Concentration=value)

ggplot(filter(Propionate_stool.melt.CT, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [mg/ml]') + 
  scale_fill_manual(labels=c("Propionat"), values = c("steelblue2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

```

Butyrat

```{r}
Butyrate_stool.melt.CT <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('Butyrate'))
Butyrate_stool.melt.CT <- rename(Butyrate_stool.melt.CT, SCFA=variable)
Butyrate_stool.melt.CT <- rename(Butyrate_stool.melt.CT, Concentration=value)

ggplot(filter(Butyrate_stool.melt.CT, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [mg/ml]') + 
  scale_fill_manual(labels=c("Butyrate"), values = c("coral2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

```
Isobutyrat

```{r}
Iso.Butyrate_stool.melt.CT <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('Iso.Butyrate'))
Iso.Butyrate_stool.melt.CT <- rename(Iso.Butyrate_stool.melt.CT, SCFA=variable)
Iso.Butyrate_stool.melt.CT <- rename(Iso.Butyrate_stool.melt.CT, Concentration=value)

ggplot(filter(Iso.Butyrate_stool.melt.CT, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [mg/ml]') + 
  scale_fill_manual(labels=c("Iso.Butyrate"), values = c("deeppink"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

```

Plot p-value zwischen Sterolconverter bei einem Zeitpunkt

```{r}
ggplot(filter(Acetate_stool.melt.CT, !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= SCFA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [mg/ml]') + 
  scale_fill_manual(labels=c("Acetate"), values = c("yellowgreen"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

ggplot(filter(Propionate_stool.melt.CT, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= SCFA)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Concentration [mg/ml]') + 
  scale_fill_manual(labels=c("Propionate"), values = c("steelblue2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))

ggplot(filter(Butyrate_stool.melt.CT, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= SCFA)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Concentration [mg/ml]') + 
  scale_fill_manual(labels=c("Butyrate"), values = c("coral2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))

ggplot(filter(Iso.Butyrate_stool.melt.CT, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= SCFA)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Concentration [mg/ml]') + 
  scale_fill_manual(labels=c("Iso.Butyrate"), values = c("deeppink"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))
```


1.5 Diversitaetsanalysen SCFA- Shannon/Simpson
Daten Laden

```{r}
SCFA_stool <- read.table("/Users/student05/Downloads/SCFA_stool total SCFA.txt", sep = '\t', comment='',
                         head=T, row.names = 1)

map_alphadiv <- read.table("/Users/student05/Documents/txt dateien r/means_alpha_div.txt", sep = '\t', comment='',head = TRUE, row.names = 1)

```

Filtern fuer PRE und POST Proben

```{r}
SCFA_stool$Time <-factor(SCFA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))

SCFA_stool[1,4]<- "OU1"

SCFA_stool_pairs <- filter(SCFA_stool, Proband == "05AP" | Proband == "06WT"
                         
                         | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                         
                         | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                         
                         | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                         
                          | Proband == "32FG" | Proband == "36ER"  | Proband == "35AD"
                         
                         | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                         
                         | Proband == "41ML"  | Proband == "47OT"
                         
                         | Proband == "50DM" | Proband == "53BD" | Proband == "57MT" | Proband == "69HL")

write.table(SCFA_stool_pairs, file = '/Users/student05/Documents/SCFA/SCFA analyse/OTU SCFA analyse/SCFA_stool.pairs Shannon Simpson.txt', sep ="\t", col.names= TRUE,row.names = FALSE)

SCFA_stool_pairs_PP <- filter(SCFA_stool_pairs, Time=="PRE" | Time=="POST")

write.table(SCFA_stool_pairs_PP, file = '/Users/student05/Documents/SCFA/SCFA analyse/OTU SCFA analyse/SCFA_stool.pairs.PP Shannon Simpson.txt', sep ="\t", col.names= TRUE,row.names = FALSE)
```

Shannon und Simpson einfuegen in SCFA Datensatz

```{r}
common.ids.St <- intersect(rownames(SCFA_stool), rownames(map_alphadiv))
common.ids.St <- intersect(row.names(SCFA_stool), row.names(map_alphadiv))

SCFA_stool <- SCFA_stool[common.ids.St,]

map_alphadiv <- map_alphadiv[common.ids.St,]

SCFA_stool$Shannon <- map_alphadiv$Shannon

SCFA_stool$Simpson <- map_alphadiv$Simpson
```

Korrelationsanalysen zwischen SCFA und Shannon
Erstellen von Matrix und Loop, filtern fuer Signifikanz


```{r}
corr_colnames_SCFA <-colnames(SCFA_stool[,6:9])

corr_spearman_Shannon_SCFA <- data.frame()

for( i in unique(corr_colnames_SCFA)) {
  
tmp <- filter(SCFA_stool, !is.na(i))
  
x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))

y = t(as.matrix(tmp$Shannon) )
  
tmp_corr_spearman <- cor.test(x, y, method="spearman")

rho = tmp_corr_spearman$estimate
  
p = tmp_corr_spearman$p.value
  
z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
  
w = t(as.matrix(subset(filter(tmp, Time == "PRE"))$Shannon))
  
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")

rho_PRE = tmp_corr_spearman_PRE$estimate
  
p_PRE = tmp_corr_spearman_PRE$p.value

r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
  
s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Shannon))
  
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
rho_POST = tmp_corr_spearman_POST$estimate
  
p_POST = tmp_corr_spearman_POST$p.value

a = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "FOLLOW-UP"))[,i],as.numeric)))
  
b = t(as.matrix(subset(filter(tmp, Time == "FOLLOW-UP"))$Shannon))
  
tmp_corr_spearman_FU <- cor.test(a, b, method="spearman")
  
rho_FU = tmp_corr_spearman_FU$estimate
  
p_FU = tmp_corr_spearman_FU$p.value
 
nrow = nrow(corr_spearman_Shannon_SCFA)+1

corr_spearman_Shannon_SCFA[nrow,"Div"] = "Shannon"
  
corr_spearman_Shannon_SCFA[nrow, "column"] = i
  
corr_spearman_Shannon_SCFA[nrow, "rho"] = rho
  
corr_spearman_Shannon_SCFA[nrow, "p.value"] = p
  
corr_spearman_Shannon_SCFA[nrow, "rho_PRE"] = rho_PRE
  
corr_spearman_Shannon_SCFA[nrow, "p.value_PRE"] = p_PRE
  
corr_spearman_Shannon_SCFA[nrow, "rho_POST"] = rho_POST
  
corr_spearman_Shannon_SCFA[nrow, "p.value_POST"] = p_POST
  
corr_spearman_Shannon_SCFA[nrow, "rho_FU"] = rho_FU
  
corr_spearman_Shannon_SCFA[nrow, "p.value_FU"] = p_FU
}

corr_spearman_Shannon_SCFA$p.adjusted <- p.adjust(corr_spearman_Shannon_SCFA$p.value, method = "BH", n = 5)

corr_spearman_Shannon_SCFA$p.adjusted_PRE <-p.adjust(corr_spearman_Shannon_SCFA$p.value_PRE, method = "BH", n = 5)

corr_spearman_Shannon_SCFA$p.adjusted_POST <- p.adjust(corr_spearman_Shannon_SCFA$p.value_POST, method = "BH", n = 5)

corr_spearman_Shannon_SCFA$p.adjusted_FU <- p.adjust(corr_spearman_Shannon_SCFA$p.value_FU, method = "BH", n = 5)


corr_sig_Shannon_SCFA <- filter(corr_spearman_Shannon_SCFA, p.adjusted < 0.05 | p.adjusted_PRE < 0.5 | p.adjusted_POST < 0.5 | p.adjusted_FU < 0.5)

write.table(corr_sig_Shannon_SCFA, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/SCFA.Shannon.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

-> no SCFA has signficicant p-value 
 Acetate has the best p-value with 0.06 < 0.05
 
Plot Korrelation Acetat/Total scfa und Shannon
 
```{r}
ggplot(SCFA_stool, aes(x=Acetate, y=Shannon)) + geom_point(aes(color=Time)) +
scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') +
ylab('Shannon-Index')

ggplot(SCFA_stool, aes(x=Total.SCFA, y=Shannon)) + geom_point(aes(color=Time)) +
  scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total SCFA Concentration [mg/ml]') +
  ylab('Shannon-Index')

SCFA_stool <- subset(filter(SCFA_stool, !Time== 'FOLLOW-UP'))

ggscatter(SCFA_stool_pairs_PP, x='Acetate', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Acetate Concentration [mg/ml]', ylab = 'Shannon-Index') +
  facet_wrap(~Time, scales = "free_x")+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(SCFA_stool, x='Total.SCFA', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(0, 7),xlab= 'Total SCFA Concentration [µmol/g DW]', ylab = 'Shannon-Index')+ 
facet_wrap(~Time, scales = "free_x")+
  theme(legend.position="none")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))
```
 
Korrelationsanalysen zwischen SCFA und Simpson
Erstellen von Matrix und Loop, filtern fuer Signifikanz

```{r}
corr_spearman_Simpson_SCFA <- data.frame()

for( i in unique(corr_colnames_SCFA)) {
  
tmp <- filter(SCFA_stool, !is.na(i))

x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))

y = t(as.matrix(tmp$Simpson))
 
tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
rho = tmp_corr_spearman$estimate
  
p = tmp_corr_spearman$p.value
 
z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
  
w = t(as.matrix (subset(filter(tmp, Time == "PRE"))$Simpson))
  
tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")

rho_PRE = tmp_corr_spearman_PRE$estimate
  
p_PRE = tmp_corr_spearman_PRE$p.value

r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
  
s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Simpson))
  
tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
rho_POST = tmp_corr_spearman_POST$estimate
  
p_POST = tmp_corr_spearman_POST$p.value

a = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "FOLLOW-UP"))[,i],as.numeric)))
  
b = t(as.matrix(subset(filter(tmp, Time == "FOLLOW-UP"))$Simpson))
  
tmp_corr_spearman_FU <- cor.test(a, b, method="spearman")
  
rho_FU = tmp_corr_spearman_FU$estimate
  
p_FU = tmp_corr_spearman_FU$p.value

nrow = nrow(corr_spearman_Simpson_SCFA)+1

corr_spearman_Simpson_SCFA[nrow,"Div"] = "Simpson"

corr_spearman_Simpson_SCFA[nrow, "column"] = i

corr_spearman_Simpson_SCFA[nrow, "rho"] = rho

corr_spearman_Simpson_SCFA[nrow, "p.value"] = p

corr_spearman_Simpson_SCFA[nrow, "rho_PRE"] = rho_PRE

corr_spearman_Simpson_SCFA[nrow, "p.value_PRE"] = p_PRE

corr_spearman_Simpson_SCFA[nrow, "rho_POST"] = rho_POST

corr_spearman_Simpson_SCFA[nrow, "p.value_POST"] = p_POST

corr_spearman_Simpson_SCFA[nrow, "rho_FU"] = rho_FU

corr_spearman_Simpson_SCFA[nrow, "p.value_FU"] = p_FU

}

corr_spearman_Simpson_SCFA$p.adjusted <- p.adjust(corr_spearman_Simpson_SCFA$p.value,method = "BH", n = 5)

corr_spearman_Simpson_SCFA$p.adjusted_PRE <-p.adjust(corr_spearman_Simpson_SCFA$p.value_PRE, method = "BH", n = 5)

corr_spearman_Simpson_SCFA$p.adjusted_POST <- p.adjust(corr_spearman_Simpson_SCFA$p.value_POST, method = "BH", n = 5)

corr_spearman_Simpson_SCFA$p.adjusted_FU <- p.adjust(corr_spearman_Simpson_SCFA$p.value_FU, method = "BH", n = 5)


corr_sig_Simpson_SCFA <- filter(corr_spearman_Simpson_SCFA, p.adjusted < 0.05 | p.adjusted_PRE < 0.5 | p.adjusted_POST < 0.5 | p.adjusted_FU < 0.5)

write.table(corr_sig_Simpson_SCFA, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/SCFA.Simpson.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```
no metadata has significant p-value
Propionat has lowest p-value 0.10 > 0.05

Plot metadata fuer Propionate/Total scfa und Simpson

```{r}
ggplot(SCFA_stool, aes(x=Propionate, y=Simpson)) + geom_point(aes(color=Time))+
 scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') +
 ylab('Simpson-Index')

ggplot(SCFA_stool, aes(x=Total.SCFA, y=Simpson)) + geom_point(aes(color=Time))+
  scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total SCFA Concentration [mg/ml]') +
  ylab('Simpson-Index')

ggscatter(SCFA_stool, x='Propionate', y='Simpson', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'),
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Propionate Concentration [mg/ml]', ylab = 'Simpson-Index') + 
facet_wrap(~Time)

ggscatter(SCFA_stool, x='Total.SCFA', y='Simpson', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'),
add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
cor.method = 'spearman', xlab= 'Total SCFA Concentration [mg/ml]', ylab = 'Simpson-Index') + 
facet_wrap(~Time)

ggscatter(SCFA_stool, x='Total.SCFA', y='Simpson',
          add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
          cor.method = 'spearman', xlab= 'Total SCFA Concentration [µmol/g]', ylab = 'Simpson-Index') 
```

Daten sichern

```{r}
corr_aphadiv_SCFA <- data_frame()

corr_aphadiv_SCFA <-bind_rows(corr_spearman_Shannon_SCFA,corr_spearman_Simpson_SCFA)

write.table(corr_aphadiv_SCFA, file = "/Users/student05/Documents/SCFA/SCFA analyse/OTU SCFA analyse/corr_alphadiv_SCFA.txt", sep= "\t", col.names = TRUE, row.names = FALSE)

```

Shannon and Simpson-Index je Time Point und "high concentration" Probands 

```{r}

SCFA_stool_con <- read.table("/Users/student05/Documents/SCFA/SCFA Tabelle Phenotypen.txt", sep = '\t', comment='',head=T, row.names = 1)

common.ids.St <- intersect(rownames(SCFA_stool_con), rownames(map_alphadiv))
common.ids.St <- intersect(row.names(SCFA_stool_con), row.names(map_alphadiv))

SCFA_stool_con <- SCFA_stool_con[common.ids.St,]

map_alphadiv <- map_alphadiv[common.ids.St,]

```

Erstellen einer Liste fuer comparisons in boxplots

```{r}
comparison_con <- list(c("high concentrations", "normal concentrations"))
```

Wilcoxon Test zwischen Phaenotypen und Shannon + Boxplot

```{r}
pairwise.wilcox.test(subset(filter(SCFA_stool_con, Time == "PRE"))$Shannon, subset(filter(SCFA_stool_con, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

ggplot(subset(filter(SCFA_stool_con)), aes(x=Phenotype, y=Shannon)) + xlab('Phenotype') + ylab('Shannon-Index')+
geom_boxplot(fill ='whitesmoke', color = 'black') +
  geom_dotplot(binaxis = ' y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') + 
  facet_wrap(~Time) +
stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))

```

Wilcoxon test zwischen Phaenotypen und Simpson + Boxplot

```{r}
pairwise.wilcox.test(subset(filter(SCFA_stool_con, Time == "PRE"))$Simpson, subset(filter(SCFA_stool_con, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

ggplot(subset(filter(SCFA_stool_con)), aes(x=Phenotype, y=Simpson)) + xlab('Phenotype') + ylab('Simpson-Index')+ 
  geom_boxplot(fill ='whitesmoke', color = 'black') +
  geom_dotplot(binaxis = ' y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') + 
  facet_wrap(~Time) + 
  stat_compare_means(method = "wilcox.test",comparisons = comparison_con, paired = FALSE, aes(label = ..p.signif..))

```

1.6 Relative Abundance SCFA-Analyse
Laden, filtern fuer high abundant taxa und sichern der Metadaten 

```{r}
L6_rarefied <- read.table("/Users/student05/Documents/Mappingfile_16SrRNA_BC22_L6.txt", sep= '\t', comment='', head=T)

L6_rarefied <- filter(L6_rarefied, Bodysite == "Stool")

row.names(L6_rarefied) <- L6_rarefied$X.SampleID

L6_rarefied <- L6_rarefied[,-c(1:18)]

L6_taxa <- L6_rarefied[, colSums(L6_rarefied > 0.01) > 10]

L6_taxa <- L6_taxa %>% select(-starts_with("Unassigned"))

L6_taxa<- sweep(L6_taxa, 1, rowSums(L6_taxa),'/')

map_KD <- read.table("/Users/student05/Documents/Mappingfile_16SrRNA_BC22.txt", sep ='\t', comment='', head=T,
row.names = 1)

L6_taxa <- rownames_to_column(L6_taxa, "SampleID")

map_KD <- rownames_to_column(map_KD, "SampleID")

L6_metadata_taxa <- merge(map_KD, L6_taxa, by.x=c("SampleID"), by.y=c("SampleID"))
L6_metadata_taxa <- L6_metadata_taxa[,-c(2,3,10:15)]
L6_metadata_taxa <- L6_metadata_taxa[,-c(9)]


write.table(L6_metadata_taxa, file = '/Users/student05/Documents/relative abundance/L6_metadata_taxa_strict_stool.txt', sep = "\t", col.names = TRUE,row.names = FALSE)
```

Filtern des Datensatzes mit der realtive abundance nach den Zeitpunkten, Bestimmung der Means je Zeitpunkt
Zusammenfuegen der Datensätze

```{r}
relab <- read.table("/Users/student05/Documents/relative abundance/L6_metadata_taxa_strict_stool.txt", sep = '\t', comment='', head=T)

relab_PRE <- filter(relab, Time == "PRE")

relab_POST <- filter(relab, Time == "POST")

relab_FU <- filter(relab, Time == "FOLLOW-UP")

relab_means_PRE <- aggregate(relab_PRE[, 10:90], list(relab_PRE$Proband), mean)

relab_means_PRE['Time'] = 'PRE'

relab_means_PRE <- rename(relab_means_PRE, Proband=Group.1)

relab_means_POST <- aggregate(relab_POST[, 10:90], list(relab_POST$Proband), mean)

relab_means_POST['Time'] = 'POST'

relab_means_POST <- rename(relab_means_POST, Proband=Group.1)

relab_means_FU <- aggregate(relab_FU[, 10:90], list(relab_FU$Proband), mean)

relab_means_FU['Time'] = 'FOLLOW-UP'

relab_means_FU <- rename(relab_means_FU, Proband=Group.1)

relab_means <- data_frame()

relab_means <- bind_rows(relab_means_PRE, relab_means_POST, relab_means_FU)

relab_means <- relab_means[, c(1, 83, 2:82)]

ncol(relab_means)


write.table(relab_means, file = '/Users/student05/Documents/relative abundance/relab_means_per_timepoint.txt',sep = "\t", col.names = TRUE, row.names = FALSE)

```

Umbenennen der Spalten

```{r}
relab_means <- read.table('/Users/student05/Documents/relative abundance/relab_means_per_timepoint.txt', sep ='\t', comment='', head=T)

relab_means_melt <- melt(relab_means, id=c('Proband', 'Time'))

relab_means_melt <- rename(relab_means_melt, Taxa=variable)

relab_means_melt <- rename(relab_means_melt, Relative_Abundance=value)
```

Subset phylum und genus level, sichern der Daten

```{r}
relab_phylum <- subset(relab_means_melt, !grepl("g__|f__|o__|c__", relab_means_melt$Taxa))

relab_phylum <- subset(relab_phylum, !grepl("k__Archaea", relab_phylum$Taxa))

relab_phylum$Time <- factor(relab_phylum$Time, levels=c('PRE','POST','FOLLOW-UP'))

relab_phylum_spread <- spread(relab_phylum, Taxa, Relative_Abundance, sep = NULL)

relab_genus <- subset(relab_means_melt, grepl("g__", relab_means_melt$Taxa))

relab_genus <- subset(relab_genus, !grepl("k__Archaea", relab_genus$Taxa))

relab_genus$Time <- factor(relab_genus$Time, levels = c('PRE','POST','FOLLOW-UP'))

relab_genus_spread <- spread(relab_genus, Taxa, Relative_Abundance, sep = NULL)

 write.table(relab_phylum_spread, file = '/Users/student05/Documents/relative abundance/relab_phylum.txt', sep= "\t", col.names = TRUE, row.names = FALSE)

write.table(relab_genus_spread, file = '/Users/student05/Documents/relative abundance/relab_genus.txt', sep ="\t", col.names = TRUE, row.names = FALSE)

```
Testen der Taxa auf Normalverteilung, Phylum und Genus
Anschließendes Filtern nach Normalverteilung

```{r}
test_normdist_phylum <- data.frame()

phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])

for (i in phylum_colnames) {
  
  fit <- shapiro.test(relab_phylum_spread[,i])
  
  p = fit$p.value
  
  nrow = nrow(test_normdist_phylum)+1
  
  test_normdist_phylum[nrow, "p.value"] = p
  
  test_normdist_phylum[nrow, "column"] = i
  
}

test_normdist_genus <- data_frame()

genus_colnames <-colnames(relab_genus_spread[, c(3:31)])

for (i in genus_colnames) {
  
  fit <- shapiro.test(relab_genus_spread[,i])
  
  p = fit$p.value
  
  nrow = nrow(test_normdist_genus)+1
  
  test_normdist_genus[nrow, "p.value"] = p
  
  test_normdist_genus[nrow, "column"] = i
  
}


normdist_phylum <- filter(test_normdist_phylum, p.value > 0.05)
normdist_genus <- filter(test_normdist_genus, p.value > 0.05)
```
-> nur Bacteroidetes, Bacteroides, Dorea, Blautia, Faecalibacterium normalverteilt

Korrelationsanalysen mit SCFA

Synchonisieren der Metadaten

```{r}
relab_phylum_ID <- relab_phylum_spread

relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))

row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID

relab_genus_ID <- relab_genus_spread

relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))

row.names(relab_genus_ID) <- relab_genus_ID$SampleID

SCFA_stool <- read.table("/Users/student05/Downloads/SCFA_stool total SCFA.txt", sep = '\t', comment='',head=T, row.names = 1)

View(SCFA_stool)

SCFA_stool<- add.rownames(SCFA_stool, "SampleID")

SCFA_stool$Time <-factor(SCFA_stool$Time, levels = c("PRE", "POST", "FOLLOWUP"))

SCFA_stool[1,3]<- "PRE"

SCFA_stool[1,4]<- "OU1"

SCFA_stool <- mutate(SCFA_stool, SampleID1 = paste(Proband, Time, sep = "."))

row.names(SCFA_stool) <- SCFA_stool$SampleID1

common.ids.relab <- intersect(rownames(SCFA_stool), rownames(relab_phylum_ID))

SCFA_stool <- SCFA_stool[common.ids.relab,]

relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]

write.table(SCFA_stool, file = '/Users/student05/Documents/SCFA/SCFA_stool_total.txt', sep= "\t", col.names = TRUE, row.names = FALSE)

```

Erstellen einer Matrix zum testen der gewuenschten Daten, hinzufuegen von einem Pseudocount 0.00001 und log-Transformation

```{r}
phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])

relab_phylum_ID_log <- relab_phylum_ID[,c(3:8)] + 0.1

relab_phylum_ID_log <- log10(relab_phylum_ID_log)

phylum_SCFA <- cbind(relab_phylum_ID_log, SCFA_stool[, c(1, 3:5, 7:11)])

phylum_SCFA$Time <- factor(phylum_SCFA$Time, levels = c("PRE", "POST"))
```

Loop Korrelationsanalyse Acetat und Phylum-level

```{r}
corr_map_phylum_Ac <- filter(phylum_SCFA, !is.na(Acetate))

corr_spearman_Phylum_Ac <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_Ac, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Acetate
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Acetate
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

 r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Acetate
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_Ac)+1

  corr_spearman_Phylum_Ac[nrow,"SCFA"] <- "Acetate"
  
  corr_spearman_Phylum_Ac[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Ac[nrow, "p.value"] = p
  
  corr_spearman_Phylum_Ac[nrow, "rho"] = rho
  
  corr_spearman_Phylum_Ac[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Ac[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Ac[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Ac[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Ac$p.adjusted <- p.adjust(corr_spearman_Phylum_Ac$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_Ac$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Ac$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_Ac$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Ac$p.value_POST, method = "BH", n = 35)

corr_sig_Phylum_Ac <- filter(corr_spearman_Phylum_Ac, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_Ac, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.Ac.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von Acetat und phylum-level

```{r}
phylum_SCFA$Time <- factor(phylum_SCFA$Time, levels = c("PRE", "POST"))
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Acetate)) + 
geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)
 

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time, scales = "free_x")

ggscatter(phylum_SCFA, x='Acetate', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance p__Bacteroidetes')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_SCFA, x='Acetate', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance p__Bacteroidetes')+

  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)

ggscatter(phylum_SCFA, x='Acetate', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_SCFA, x='Acetate', y='k__Bacteria.p__Verrucomicrobia', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")
```

Loop Korrelationsanalyse Propionat und Phylum-level

```{r}
corr_map_phylum_Pr <- filter(phylum_SCFA, !is.na(Propionate))

corr_spearman_Phylum_Pr <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_Pr, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Propionate

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Propionate
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Propionate
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_Pr)+1

  corr_spearman_Phylum_Pr[nrow,"SCFA"] <- "Propionate"
  
  corr_spearman_Phylum_Pr[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Pr[nrow, "p.value"] = p
  
  corr_spearman_Phylum_Pr[nrow, "rho"] = rho
  
  corr_spearman_Phylum_Pr[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Pr[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Pr[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Pr[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Pr$p.adjusted <- p.adjust(corr_spearman_Phylum_Pr$p.value, method = "BH", n = 35) 
corr_spearman_Phylum_Pr$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Pr$p.value_PRE, method = "BH", n = 35)
corr_spearman_Phylum_Pr$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Pr$p.value_POST, method = "BH", n = 35)

corr_sig_Phylum_Pr <- filter(corr_spearman_Phylum_Pr, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_Pr, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.Pr.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Propionat und phylum-level

```{r}
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)

```

Loop Butyrat und Phylum-level

```{r}
corr_map_phylum_Bu <- filter(phylum_SCFA, !is.na(Butyrate))

corr_spearman_Phylum_Bu <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_Bu, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Butyrate

  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Butyrate
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Butyrate
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_Bu)+1

  corr_spearman_Phylum_Bu[nrow,"SCFA"] <- "Butyrate"
  
  corr_spearman_Phylum_Bu[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Bu[nrow, "p.value"] = p
  
  corr_spearman_Phylum_Bu[nrow, "rho"] = rho
  
  corr_spearman_Phylum_Bu[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Bu[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Bu[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Bu[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Bu$p.adjusted <- p.adjust(corr_spearman_Phylum_Bu$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_Bu$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Bu$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_Bu$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Bu$p.value_POST, method = "BH", n = 35)

corr_sig_Phylum_Bu <- filter(corr_spearman_Phylum_Bu, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_Bu, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.Bu.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Butyrate und phylum-level

```{r}
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+ 
  scale_facet_wrap_discrete(limits = c("PRE", "POST"))

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)

```

Loop fuer Isobutyrat und Phylum-level

```{r}
corr_map_phylum_IB <- filter(phylum_SCFA, !is.na(Iso.Butyrate))

corr_spearman_Phylum_IB <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_IB, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Iso.Butyrate

  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Iso.Butyrate
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Iso.Butyrate
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_IB)+1

  corr_spearman_Phylum_IB[nrow,"SCFA"] <- "Iso.Butyrate"
  
  corr_spearman_Phylum_IB[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_IB[nrow, "p.value"] = p

  corr_spearman_Phylum_IB[nrow, "rho"] = rho
  
  corr_spearman_Phylum_IB[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_IB[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_IB[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_IB[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_IB$p.adjusted <- p.adjust(corr_spearman_Phylum_IB$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_IB$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_IB$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_IB$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_IB$p.value_POST, method = "BH", n = 35)

corr_sig_Phylum_IB <- filter(corr_spearman_Phylum_IB, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_IB, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.IB.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Isobutyrat und Phylum-level

```{r}
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)
```

Loop Total SCFA und Phylum-level

```{r}
corr_map_phylum_TS <- filter(phylum_SCFA, !is.na(Total.SCFA))

corr_spearman_Phylum_TS <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_TS, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$Total.SCFA

  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Total.SCFA
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Total.SCFA
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_TS)+1

  corr_spearman_Phylum_TS[nrow,"SCFA"] <- "Total.SCFA"
  
  corr_spearman_Phylum_TS[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_TS[nrow, "p.value"] = p
  
  corr_spearman_Phylum_TS[nrow, "rho"] = rho
  
  corr_spearman_Phylum_TS[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_TS[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_TS[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_TS[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_TS$p.adjusted <- p.adjust(corr_spearman_Phylum_TS$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_TS$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_TS$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_TS$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_TS$p.value_POST, method = "BH", n = 35)

corr_sig_Phylum_TS <- filter(corr_spearman_Phylum_TS, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_TS, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Phylum.TS.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Total SCFA und phylum-level

```{r}
ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Firmicutes, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Actinobacteria, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Bacteroidetes, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Proteobacteria, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Tenericutes, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)

ggplot(phylum_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)
```

Sichern der Daten

```{r}
corr_phylum_SCFA <- data_frame()

corr_phylum_SCFA <- bind_rows(corr_spearman_Phylum_Ac,corr_spearman_Phylum_Pr,corr_spearman_Phylum_Bu, corr_spearman_Phylum_IB, corr_spearman_Phylum_TS)
                                 

write.table(corr_phylum_SCFA, file = '/Users/student05/Documents/relative abundance/corr_phylum_SCFA_all_PRE_POST.txt',sep = "\t", col.names = TRUE, row.names = FALSE)

```

Analysen mit Genus-Level

Erstellen und filtern der Matrix, Log-Transformation und hinzufuegen von Pseudocount 0.00001

```{r}
 genus_colnames <- colnames(relab_genus_spread[, c(3:31)])

relab_genus_ID_log <- relab_genus_ID[,c(3:31)] + 0.00001

relab_genus_ID_log <- log10(relab_genus_ID_log)

genus_SCFA <- cbind(relab_genus_ID_log, SCFA_stool[, c(1:10)])

```

Loop Acetate und Genus-Level

```{r}
corr_map_genus_Ac <- filter(genus_SCFA, !is.na(Acetate))

corr_spearman_genus_Ac <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_Ac, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Acetate
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Acetate
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Acetate
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_Ac)+1
  
  corr_spearman_genus_Ac[nrow,"SCFA"] = "Acetate"
  
  corr_spearman_genus_Ac[nrow, "Genus"] = i
  
  corr_spearman_genus_Ac[nrow, "p.value"] = p
  
  corr_spearman_genus_Ac[nrow, "rho"] = rho
  
  corr_spearman_genus_Ac[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_Ac[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_Ac[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_Ac[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_Ac$p.adjusted <- p.adjust(corr_spearman_genus_Ac$p.value, method = "BH", n = 35)

corr_spearman_genus_Ac$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Ac$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_Ac$p.adjusted_POST <- p.adjust(corr_spearman_genus_Ac$p.value_POST, method = "BH", n = 35)

corr_sig_genus_Ac <- filter(corr_spearman_genus_Ac, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_Ac, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.Ac.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Acetate und genus-level

```{r}
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time)

ggscatter(genus_SCFA, x='Acetate', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -2), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(genus_SCFA, x='Acetate', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -2), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance g__Oscillospira')+theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [nmol/mg DW]') + 
  ylab('log10 (Relative Abundance g__Collinsella)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Faecalibacterium )')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [µmol/g]') + 
  ylab('log10 (Relative Abundance g__Akkermansia )')+
  facet_wrap(~Time)

ggscatter(genus_SCFA, x='Acetate', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -1.1), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(genus_SCFA, x='Acetate', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -1.1), xlab= 'Acetate Concentration [µmol/g DW]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Collinsella )')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=Acetate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Acetate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Bacteroides )')+
  facet_wrap(~Time)

```

Loop Propionat und Genus-Level

```{r}
corr_map_genus_Pr <- filter(genus_SCFA, !is.na(Propionate))

corr_spearman_genus_Pr <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_Pr, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Propionate
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Propionate
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Propionate
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_Pr)+1
  
  corr_spearman_genus_Pr[nrow,"SCFA"] = "Propionate"
  
  corr_spearman_genus_Pr[nrow, "Genus"] = i
  
  corr_spearman_genus_Pr[nrow, "p.value"] = p
  
  corr_spearman_genus_Pr[nrow, "rho"] = rho
  
  corr_spearman_genus_Pr[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_Pr[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_Pr[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_Pr[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_Pr$p.adjusted <- p.adjust(corr_spearman_genus_Pr$p.value, method = "BH", n = 35)

corr_spearman_genus_Pr$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Pr$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_Pr$p.adjusted_POST <- p.adjust(corr_spearman_genus_Pr$p.value_POST, method = "BH", n = 35)


corr_sig_genus_Pr <- filter(corr_spearman_genus_Pr, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_Pr, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.Pr.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Propionat und genus-level

```{r}
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=Propionate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Propionate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Akkermansia)')+
  facet_wrap(~Time)


```

Loop Butyrat und Genus-Level

```{r}
corr_map_genus_Bu <- filter(genus_SCFA, !is.na(Butyrate))

corr_spearman_genus_Bu <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_Bu, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Butyrate
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman", paied = T)
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Butyrate
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman", paied = T)
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Butyrate
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman", paied = T)
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_Bu)+1
  
  corr_spearman_genus_Bu[nrow,"SCFA"] = "Butyrate"
  
  corr_spearman_genus_Bu[nrow, "Genus"] = i
  
  corr_spearman_genus_Bu[nrow, "p.value"] = p
  
  corr_spearman_genus_Bu[nrow, "rho"] = rho
  
  corr_spearman_genus_Bu[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_Bu[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_Bu[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_Bu[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_Bu$p.adjusted <- p.adjust(corr_spearman_genus_Bu$p.value, method = "BH", n = 35)

corr_spearman_genus_Bu$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Bu$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_Bu$p.adjusted_POST <- p.adjust(corr_spearman_genus_Bu$p.value_POST, method = "BH", n = 35)

corr_sig_genus_Bu <- filter(corr_spearman_genus_Bu, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_Bu, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.Bu.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Butyrat und genus-level

```{r}
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus., x=Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__.Ruminococcus)')+
  facet_wrap(~Time)

```

Loop Isobutyrat und Genus-Level

```{r}
corr_map_genus_BI <- filter(genus_SCFA, !is.na(Iso.Butyrate))

corr_spearman_genus_BI <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_BI, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Iso.Butyrate
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman", paired = T)
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Iso.Butyrate
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman", paired = T)
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Iso.Butyrate
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman", paied = T)
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_BI)+1
  
  corr_spearman_genus_BI[nrow,"SCFA"] = "Iso.Butyrate"
  
  corr_spearman_genus_BI[nrow, "Genus"] = i
  
  corr_spearman_genus_BI[nrow, "p.value"] = p
  
  corr_spearman_genus_BI[nrow, "rho"] = rho
  
  corr_spearman_genus_BI[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_BI[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_BI[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_BI[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_BI$p.adjusted <- p.adjust(corr_spearman_genus_BI$p.value, method = "BH", n = 35)

corr_spearman_genus_BI$p.adjusted_PRE <- p.adjust(corr_spearman_genus_BI$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_BI$p.adjusted_POST <- p.adjust(corr_spearman_genus_BI$p.value_POST, method = "BH", n = 35)


corr_sig_genus_BI <- filter(corr_spearman_genus_BI, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_BI, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.BI.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Isobutyrat genus-level

```{r}
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance f__Coriobacteriaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__RF32.f__.g__, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance c__Alphaproteobacteria.o__RF32)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__.g__, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance c__Clostridia.o__Clostridiales)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus, x=Iso.Butyrate)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Iso.Butyrate Concentration [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Coprococcus)')+
  facet_wrap(~Time)


```

Loop total SCFA und Genus-Level

```{r}
corr_map_genus_TS <- filter(genus_SCFA, !is.na(Total.SCFA))

corr_spearman_genus_TS <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_TS, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Total.SCFA
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman", paired = T)
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Total.SCFA
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman", paired = T)
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Total.SCFA
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman", paired = T)
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_TS)+1
  
  corr_spearman_genus_TS[nrow,"SCFA"] = "Total.SCFA"
  
  corr_spearman_genus_TS[nrow, "Genus"] = i
  
  corr_spearman_genus_TS[nrow, "p.value"] = p
  
  corr_spearman_genus_TS[nrow, "rho"] = rho
  
  corr_spearman_genus_TS[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_TS[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_TS[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_TS[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_TS$p.adjusted <- p.adjust(corr_spearman_genus_TS$p.value, method = "BH", n = 35)

corr_spearman_genus_TS$p.adjusted_PRE <- p.adjust(corr_spearman_genus_TS$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_TS$p.adjusted_POST <- p.adjust(corr_spearman_genus_TS$p.value_POST, method = "BH", n = 35)

corr_sig_genus_TS <- filter(corr_spearman_genus_TS, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_TS, file = '/Users/student05/Documents/SCFA/SCFA analyse/SCFA Tabelle/Genus.TS.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten total SCFA und genus-level

```{r}
ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') + 
  ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)

ggplot(genus_SCFA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=Total.SCFA)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total.SCFA [mg/ml]') + 
  ylab('log10 (Relative Abundance g__Akkermansia)')+
  facet_wrap(~Time)

```

Daten sichern


```{r}
corr_genus_SCFA <- data_frame()

corr_genus_SCFA <- bind_rows(corr_spearman_genus_Ac, corr_spearman_genus_Pr,corr_spearman_genus_Bu, corr_spearman_genus_BI, corr_spearman_genus_TS)

write.table(corr_genus_SCFA, file = '/Users/student05/Documents/relative abundance/corr_genus_SCFA_all_PRE_POST.txt', sep = "\t", col.names = TRUE, row.names = FALSE)

corr_genus_SCFA_sig <- data_frame()

corr_genus_SCFA_sig <- bind_rows(corr_sig_genus_Ac, corr_sig_genus_Pr, corr_sig_genus_Bu, corr_sig_genus_BI, corr_sig_genus_TS)

write.table(corr_genus_SCFA_sig, file ='/Users/student05/Documents/relative abundance/corr_genus_SCFA_sig_PRE_POST.txt',sep ="\t", col.names = TRUE, row.names = FALSE)

```

1.7 Testen von Unterschieden im relativen Vorkommen der Taxa von Probanden mit sehr hohen SCFA-Konzentrationen und normalen SCFA-Konzentrationen

Laden der Metadaten

```{r}
SCFA_Pt <- read.table("/Users/student05/Documents/SCFA/SCFA Tabelle Phenotypen.txt", sep ='\t',comment='',head=T)

SCFA_Pt[ ,6] <- NULL
```

Synchonisieren der Metadaten

```{r}
SCFA_Pt <- mutate(SCFA_Pt, SampleID1 = paste(Proband, Time, sep = "."))

row.names(SCFA_Pt) <- SCFA_Pt$SampleID1

common.ids.relab <- intersect(rownames(SCFA_Pt), rownames(relab_phylum_ID))

SCFA_Pt <- SCFA_Pt[common.ids.relab,]

relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]
```

Matrix erstellen, filtern, hinzufuegen von log und Psedocount

```{r}
write.table(SCFA_stool, file = '/Users/student05/Documents/SCFA/SCFA_Pt.txt', sep= "\t", col.names = TRUE, row.names = FALSE)

relab_phylum_ID_log <- relab_phylum_ID[,c(3:8)] + 0.1

relab_phylum_ID_log <- log10(relab_phylum_ID_log)

phylum_Pt <- cbind(relab_phylum_ID_log, SCFA_Pt[, c(1, 3:5, 7:11, 14)])
```

Phylum-Differenzen zwsichen den Phaenotypen

```{r}
comparison_con <- list(c("low concentrations", "high concentrations"))

pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Firmicutes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Firmicutes)') +
geom_boxplot(fill = 'whitesmoke', color="black") + 
geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)

phylum_Pt$k__Bacteria.p__Actinobacteria

pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Actinobacteria, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Actinobacteria)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Actinobacteria)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)

phylum_Pt$k__Bacteria.p__Bacteroidetes
pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)


ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Bacteroidetes)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)



pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Proteobacteria, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Proteobacteria)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Proteobacteria)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)



pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Tenericutes, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Tenericutes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Tenericutes)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)



pairwise.wilcox.test(subset(filter(phylum_Pt, Time == "PRE"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_Pt, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

ggplot(subset(filter(phylum_Pt)), aes(x=Phenotype,y=k__Bacteria.p__Verrucomicrobia)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Verrucomicrobia)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_con)

```
1.8 Beta-Diversitaet

Laden und filtern der Metadaten 

```{r}
otus_means <- read.table("/Users/student05/Documents/otus_means_stool 2.txt", sep ='\t',comment='',head=T)

setwd("/Users/student05/Documents/SCFA")

map_bdiv <- read.table("/Users/student05/Documents/Mappingfile_16SrRNA_BC22.txt", sep ='\t',comment='',head=T)

map_bdiv <- mutate(map_bdiv, SampleID2 = paste(Proband, Time, sep = "."))

map_bdiv <- filter(map_bdiv, Timepoint == "0U1" | Timepoint =="0U2" | Timepoint =="0U3")

map_bdiv <- filter(map_bdiv, Bodysite == "Stool")

rownames(map_bdiv) <- map_bdiv$SampleID2
```

Synchonisieren der Daten

```{r}
otus_relab <- mutate(otus_means, SampleID = paste(Proband, Time, sep = "."))

rownames(otus_relab) <- otus_relab$SampleID

otus_relab <- sweep(otus_relab[, 2:84606], 1, rowSums(otus_relab[, 2:84606]), '/')

dim(otus_relab)

common.ids.relab <- intersect(rownames(map_bdiv), rownames(otus_relab))

map_bdiv <- map_bdiv[common.ids.relab,]

otus_relab <- otus_relab[common.ids.relab,]

dim(otus_relab)

write.table(otus_relab, file = "otus_relab_bc.txt", sep = "\t", col.names = TRUE,row.names = TRUE)
```

Bray-Curtis Kalkulation

```{r}
d.bray <-vegdist(otus_relab)

matrix.bray<- as.matrix(d.bray)

write.table(matrix.bray, file = "matrix.bray.txt", sep = "\t", col.names = T, row.names = T)
```

Erstellen einer Distance-matrix
(All functions used are created by Daniel Podlesny and saved in the R Script "modify_distmat.R".
Open the script in R and run all functions. They will then be listed in the Environment.
call the function with the distance matrix for this project)

```{r}
distmat <- clean_distmat(as.data.frame(matrix.bray))

distmat <- distmat_to_long(distmat, rm_diag = TRUE)

distmat_PRE <- filter(distmat, grepl('*.PRE', distmat$row) & grepl('*.PRE', distmat$col))

distmat_POST <- filter(distmat, grepl('*.POST', distmat$row) & grepl('*.POST', distmat$col))

distmat_FU <- filter(distmat, grepl('*.FOLLOW-UP', distmat$row) & grepl('*.FOLLOW-UP', distmat$col))

distmat_PRE['Time'] = 'PRE'

distmat_POST['Time'] = 'POST'

distmat_FU['Time'] = 'FOLLOW-UP'

distmat_PREvsPOST <- data_frame()

distmat_PREvsPOST <- bind_rows(distmat_PRE, distmat_POST)

distmat_PREvsPOST$Time <- factor(distmat_PREvsPOST$Time, levels = c("PRE", "POST"))

distmat_all <- data_frame()

distmat_all <- bind_rows(distmat_PRE, distmat_POST, distmat_FU)

distmat_all$Time <- factor(distmat_all$Time, levels=c("PRE", "POST", "FOLLOW-UP"))

```

Filtern fuer Proben mit PRE und POST

```{r}
distmat_PREvsPOST_pairs <- filter(distmat_PREvsPOST, !row == "31KE.POST" & !row =="34WF.PRE" & !row == "45GL.POST" & !row == "49RJ.PRE" &!row == "54SL.POST" & !row == "70PL.PRE" & !row == "74SA.POST")

distmat_PREvsPOST_pairs <- filter(distmat_PREvsPOST_pairs, !col == "31KE.POST" &!col == "34WF.PRE" & !col == "45GL.POST" & !col == "49RJ.PRE" & !col == "54SL.POST" & !col == "70PL.PRE" & !col == "74SA.POST")

```

Wilcoxon-Test PRE und POST + boxplot

```{r}
pairwise.wilcox.test(distmat_PREvsPOST_pairs$distance, distmat_PREvsPOST_pairs$Time, p.adjust.method = "BH", paired = TRUE)

pairwise.wilcox.test(distmat_PREvsPOST_pairs$distance, distmat_PREvsPOST_pairs$Time, p.adjust.method = "BH", paired = TRUE)

ggplot(distmat_PREvsPOST_pairs, aes(x=Time, y=distance)) + xlab('Timepoint') + ylab('Bray-Curtis Dissimilarity (baseline per proband)') + 
geom_boxplot(fill='whitesmoke', color="black") + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.2) +
ggtitle('Beta-Diversity between probands before and after a 6-week Ketogenic Diet') +
stat_compare_means(comparison = list(c("PRE", "POST")), paired = TRUE, aes(label= ..p.signif..))

```

Mean und SD für PRE und POST

```{r}
mean(subset(filter(distmat_PREvsPOST_pairs, Time == "PRE"))$distance)

sd(subset(filter(distmat_PREvsPOST_pairs, Time == "PRE"))$distance)

mean(subset(filter(distmat_PREvsPOST_pairs, Time == "POST"))$distance)

sd(subset(filter(distmat_PREvsPOST_pairs, Time == "POST"))$distance)
```



2. FA-Analyse

2.1 Normalverteilung
Metadaten Laden, filtern und sortieren

```{r}
FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
                       head=T)
View(FA_stool)

FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST"))

FA_stool <- FA_stool[-c(58:64),]

FA_stool<- add_rownames(FA_stool, "SampleID1")
FA_stool.r<- add_rownames(FA_stool, "SampleID1")

row.names(FA_stool) <- FA_stool$SampleID

```

Testen auf Normalverteilung

```{r}
FA_colnames <- colnames(FA_stool[, c(7:19)])

nd.FA<- data_frame()

for (i in FA_colnames)  {
  fit <- shapiro.test(as.matrix(as.data.frame(lapply(FA_stool[,i],
                                                     as.numeric))))
  p = fit$p.value
  nrow = nrow(nd.FA)+1
  nd.FA[nrow, "column"] = i
  nd.FA[nrow, "p.value"] = round(p, 4)
}

sign.nd_FA <- filter(nd.FA, p.value > 0.05)
```

Plotten der Normalverteilungen

```{r}
ggqqplot(FA_stool$sat, ylab = "Saturated FA concentration nmol/g", xlab = "SampleID")
ggqqplot(FA_stool$mono.unsat, ylab = "Mono unsaturated concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$di.unsat, ylab = "Diunsaturated concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$less.14, ylab = " < 14 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$c18.19, ylab = "FA with 18-19 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$c14.17, ylab = "FA with 14-17 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$c20.21, ylab = "FA with 20-21 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$c22.24, ylab = "FA with 22-24 c-atoms concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool$total, ylab = "Total FA concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool.r$n.3, ylab = "Omega 3 FA concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool.r$n.6, ylab = "Omega 6 FA concentration [nmol/g]", xlab = "SampleID")
ggqqplot(FA_stool.r$ratio6.3, ylab = "Omega 6/3 ratio [nmol/g]", xlab = "SampleID")
```

Filtern nach PRE und POST Proben

```{r}
FA_stool_pairs <- filter(FA_stool, Proband == "05AP" | Proband == "06WT"
                           
                           | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                           
                           | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                           
                           | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                           
                           | Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
                           
                           | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                           
                           | Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
                           
                           | Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
                           
                           | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")



FA_stool_pairs_PP <- filter(FA_stool_pairs, Time=="PRE" | Time=="POST")


FA_stool_pairs_PPFU <- filter(FA_stool, Proband == "05AP" | Proband == "13BS" | Proband == "17SK" | Proband == "22WS" | Proband == "40WA" | Proband == "41ML" | Proband == "54SL")

```

Wilcoxon-Test zwischen den Zeitpunkten PRE und POST
Erstellen eines neuen Dataframes 

```{r}
wilcox_FA<- data_frame()

environment(filter)

for (i in FA_colnames) {
  
  tmp <- FA_stool_pairs %>% drop_na(i) 
  
  x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
  
  y <- FA_stool_pairs$Time 
  
  tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = T)
  
  p <- tmp_wilcox$p.value
  
  nrow = nrow(wilcox_FA)+1
  
  wilcox_FA[nrow, "LI"] <- i 
  
  
  wilcox_FA[nrow, "Mean PRE"] <-round(mean(subset(filter(FA_stool_pairs,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), 2, mean,  na.rm = TRUE), 4)
  
  wilcox_FA[nrow, "sd PRE"] <-round(sd(c(subset(filter(FA_stool_pairs,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), na.rm = TRUE)), 4)
  
  wilcox_FA[nrow, "Mean POST"] <-round(mean(subset(filter(FA_stool_pairs,Time == "POST")[,i],!is.na(i), na.rm = TRUE), 2, mean,  na.rm = TRUE), 4)
  
  wilcox_FA[nrow, "sd POST"] <- round(sd(c(subset(filter(FA_stool_pairs,Time == "POST")[,i],!is.na(i), na.rm = TRUE),na.rm = TRUE)), 4)
  
  wilcox_FA[nrow, "p.value"] <- round(p, 4) }


write.table(wilcox_FA, file = '/Users/student05/Documents/fa feces/fa tabellen/FA.pre.post.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Zeitpunkte zusammen

```{r}
wilcox_FA1<- data_frame()

for (i in FA_colnames) {
  
  tmp <- FA_stool_pairs %>% drop_na(i) 
  
  x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
  
  y <- FA_stool_pairs$Time 
  
  tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = T)
  
  p <- tmp_wilcox$p.value
  
  nrow = nrow(wilcox_FA1)+1
  
  wilcox_FA1[nrow, "LI"] <- i 
  
  wilcox_FA1[nrow, "Mean"] <-round(mean(subset(filter(FA_stool_pairs)[,i],!is.na(i),na.rm = TRUE), 2, mean,  na.rm = TRUE), 4)
  
  wilcox_FA1[nrow, "sd"] <-round(sd(c(subset(filter(FA_stool_pairs)[,i],!is.na(i),na.rm = TRUE), na.rm = TRUE)), 4)
  
  wilcox_FA1[nrow, "p.value"] <- round(p, 4) }

write.table(wilcox_FA1, file = '/Users/student05/Documents/fa feces/fa tabellen/FA.alltimes.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Boxplot der FA je Zeitpunkt

Melt Daten

```{r}

FA_stool.melt <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('sat', 'mono.unsat', 'di.unsat', 'more.2.unsat', 'less.14', 'c14.17', 'c18', 'c20.24', 'total'))

FA_stool.melt <- dplyr::rename(FA_stool.melt, FA=variable)
FA_stool.melt <- dplyr::rename(FA_stool.melt, Concentration=value)

ggplot(FA_stool.melt,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("saturated", "monounsaturated", "diunsaturated", "> 2 unsaturated", "< c14", "c 14-17", "c 18-19", "c 20-21", "c 22-24", "total", "iso", "anteiso"), 
                    values = c("tomato", "yellowgreen", "steelblue2", "orchid2", "deeppink", "brown4", "darkorange1", "blueviolet", "aquamarine3", "darksalmon", "cyan3", "darkgreen")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

```

Plots, die in Arbeit vorkommen

nach Saettigung

```{r}

FA_stool.melt.sat <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('sat', 'mono.unsat', 'di.unsat', 'more.2.unsat'))

FA_stool.melt.sat <- dplyr::rename(FA_stool.melt.sat, FA=variable)
FA_stool.melt.sat <- dplyr::rename(FA_stool.melt.sat, Concentration=value)

FA_stool.melt.sat$Time <- factor(FA_stool.melt.sat$Time, levels = c("PRE", "POST"))

pdf("/Users/student05/Documents/fertige Plots/FA.DB.KD.pdf",width=8, height=10)
ggplot(FA_stool.melt.sat,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/g]') + 
  geom_boxplot(width = .7, lwd=0.7)  + theme_classic()+
  scale_fill_manual(labels = c("0", "1", "2", "3-6"), 
                    values = c("#f0f9e8", "#bae4bc", "#7bccc4", "#2b8cbe")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),axis.text=element_text(size=16))+
  theme(legend.position="top")
dev.off()


```

MCTs

```{r}
FA_stool.melt.mct <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('less.14'))
FA_stool.melt.mct <- dplyr::rename(FA_stool.melt.mct, FA=variable)
FA_stool.melt.mct <- dplyr::rename(FA_stool.melt.mct, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/FA.MCT.KD.pdf",width=8, height=10)
ggplot(FA_stool.melt.mct,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Zeitpunkt') + ylab ('Konzentrationen [nmol/g]') + 
  geom_boxplot(width = .7, lwd=0.7)  + 
  scale_fill_manual(labels = c("MCT"), 
                    values = c("navy")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),axis.text=element_text(size=16))+
  theme(legend.position="top")
dev.off()

```

Kettenlaenge

```{r}
FA_stool.melt.kl <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('less.14','c14.17', 'c18', 'c20.24'))

FA_stool.melt.kl$Time <- factor(FA_stool.melt.kl$Time, levels = c("PRE", "POST"))

FA_stool.melt.kl <- dplyr::rename(FA_stool.melt.kl, FA=variable)
FA_stool.melt.kl <- dplyr::rename(FA_stool.melt.kl, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/FA.KL.KD.pdf",width=8, height=10)
ggplot(FA_stool.melt.kl,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/g]') + 
  geom_boxplot(width = .7, lwd=0.7)  + theme_classic()+
  scale_fill_manual(labels = c("> 14 c (MCT)","c 14-17", "c 18", "c 20-24"), 
                    values = c("#f1eef6", "#bdc9e1", "#74a9cf", "#0570b0")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),axis.text=element_text(size=16))+
  theme(legend.position="top")
dev.off()

```

Total FA

```{r}
FA_stool.melt.t <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('total'))
FA_stool.melt.t <- dplyr::rename(FA_stool.melt.t, FA=variable)
FA_stool.melt.t <- dplyr::rename(FA_stool.melt.t, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/FA.total.KD.pdf",width=6, height=10)
ggplot(FA_stool.melt.t,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/g]') + 
  geom_boxplot(width = .2, lwd=1)  + theme_classic()+
  scale_fill_manual(labels = c("Gesamtfettsäuren"), 
                    values = c("cornflowerblue")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),axis.text=element_text(size=16))+
  theme(legend.position="top")+
expand_limits(y=c(0, 3000))
dev.off()
```

Omega-FA

```{r}
FA_stool.melt.o <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('Omega3', 'Omega6','ratio'))
FA_stool.melt.o <- dplyr::rename(FA_stool.melt.o, FA=variable)
FA_stool.melt.o <- dplyr::rename(FA_stool.melt.o, Concentration=value)

FA_stool.melt.o$Time <- factor(FA_stool.melt.o$Time, levels = c("PRE", "POST"))

pdf("/Users/student05/Documents/fertige Plots/FA.omega.KD.pdf",width=8, height=10)
ggplot(FA_stool.melt.o,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/g]') + 
  geom_boxplot(width = .6, lwd=0.7)  + theme_classic()+
  scale_fill_manual(labels = c("alpha-Linolensäure", "Linolsäure", "Omega 6/Omega 3 Verhältnis"), 
                    values = c("#2c7fb8", "#7fcdbb", "#edf8b1")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),axis.text=element_text(size=16))+
  theme(legend.position="top")+
  expand_limits(y=c(0, 3000))
dev.off()

```

In Arbeit
Korrelation zwischen Saettigung der Fettsaeuren und Konzentration 


```{r}

FA_stool.melt.kl$chain.length <- as.integer(FA_stool.melt.kl$chain.length)
FA_stool.melt.kl <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c( 'less.14', 'c14.17', 'c18'))

FA_stool.melt.kl.pre <- subset(filter(FA_stool.melt.kl, !Time =='POST'))

FA_stool.melt.kl <- dplyr::rename(FA_stool.melt.kl, chain.length=variable)
FA_stool.melt.kl <- dplyr::rename(FA_stool.melt.kl, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/KL.Konzentration.pdf",width=8, height=10)
ggscatter(FA_stool.melt.kl, x='chain.length', y='Concentration',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', conf.int = T, 
          cor.coef = T, cor.method = 'spearman',cor.coef.coord = c(1, 2500), cor.coef.size = 5, xlab= '..', ylab = 'Konzentration [nmol/g]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
dev.off()


pdf("/Users/student05/Documents/fertige Plots/KL.Konzentration2.pdf",width=8, height=10)
ggscatter(FA_stool.melt.kl, x='chain.length', y='Concentration', add = 'reg.line', color = "grey59",fill = "lightgray",conf.int = T, 
          cor.coef = T, cor.method = 'spearman',cor.coef.coord = c(1, 2500), cor.coef.size = 8, xlab= '..', ylab = 'Konzentration [nmol/g]')+
  theme(strip.text.x = element_text(size = 20, colour = "black"))+
  theme(text = element_text(size=20),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")+
geom_point(color='black', size=2.5)
dev.off()

cor.test(subset(filter(FA_stool.melt.kl))$chain.length, subset(filter(FA_stool.melt.kl))$Concentration, method = "spearman", exact = F)

p.adjust(c(2.2e-16), method = 'BH', n=1)

```
(S = 87355, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
  rho 
0.8619349 
q-value =2.2e-16)

Boxplots einzelner FAs zu den Zeitpunkten PRE und POST

von gesaettigten FA bis Total FA

```{r}

FA_stool.melt.sat <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('sat'))
FA_stool.melt.sat <- rename(FA_stool.melt.sat, FA=variable)
FA_stool.melt.sat <- rename(FA_stool.melt.sat, Concentration=value)
ggplot(FA_stool.melt.sat,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("saturated"), 
                    values = c("tomato")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons = comparison_time)


FA_stool.melt.ms <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('mono.unsat'))
FA_stool.melt.ms <- rename(FA_stool.melt.ms, FA=variable)
FA_stool.melt.ms <- rename(FA_stool.melt.ms, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.ms,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("monounsaturated"), 
                    values = c("yellowgreen")) +
  theme(legend.position="top")+
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons = comparison_time)


FA_stool.melt.ds <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('di.unsat'))
FA_stool.melt.ds <- rename(FA_stool.melt.ds, FA=variable)
FA_stool.melt.ds <- rename(FA_stool.melt.ds, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.ds,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("diunsaturated"), 
                    values = c("steelblue2")) +
   theme(legend.position="top")+
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)


FA_stool.melt.m2u <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('more.2.unsat'))
FA_stool.melt.m2u <- rename(FA_stool.melt.m2u, FA=variable)
FA_stool.melt.m2u <- rename(FA_stool.melt.m2u, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.m2u,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("> 2 unsaturated"), 
                    values = c("orchid2")) +
   theme(legend.position="top")+
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)



FA_stool.melt.14 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('less.14'))
FA_stool.melt.14 <- rename(FA_stool.melt.14, FA=variable)
FA_stool.melt.14 <- rename(FA_stool.melt.14, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.14,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("< c14"), 
                    values = c("deeppink")) +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")



FA_stool.melt.1417 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('c14.17'))
FA_stool.melt.1417 <- rename(FA_stool.melt.1417, FA=variable)
FA_stool.melt.1417 <- rename(FA_stool.melt.1417, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.1417,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("c 14-17"), 
                    values = c("brown4")) +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)



FA_stool.melt.1417 <- melt(FA_stool_pairs, id.vars = 'Time', measure.vars = c('c14.17'))
FA_stool.melt.1417 <- rename(FA_stool.melt.1417, FA=variable)
FA_stool.melt.1417 <- rename(FA_stool.melt.1417, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.1417,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("c 14-17"), 
                    values = c("brown4")) +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")



FA_stool.melt.18 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('c18.19'))
FA_stool.melt.18 <- rename(FA_stool.melt.18, FA=variable)
FA_stool.melt.18 <- rename(FA_stool.melt.18, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.18,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("c 18-19"), 
                    values = c("darkorange1")) +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")


FA_stool.melt.20 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('c20.21'))
FA_stool.melt.20 <- rename(FA_stool.melt.20, FA=variable)
FA_stool.melt.20 <- rename(FA_stool.melt.20, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.20,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("c 20-21"), 
                    values = c("blueviolet")) +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")



FA_stool.melt.22 <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('c22.24'))
FA_stool.melt.22 <- rename(FA_stool.melt.22, FA=variable)
FA_stool.melt.22 <- rename(FA_stool.melt.22, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.22,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("c 22-24"), 
                    values = c("aquamarine3")) +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")



FA_stool.melt.t <- melt(FA_stool_pairs_PP, id.vars = 'Time', measure.vars = c('total'))
FA_stool.melt.t <- rename(FA_stool.melt.t, FA=variable)
FA_stool.melt.t <- rename(FA_stool.melt.t, Concentration=value)
comparison_time <- list(c("PRE", "POST"))
ggplot(FA_stool.melt.t,aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ FA) +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("total"), 
                    values = c("darksalmon")) +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons = comparison_time)+ theme(legend.position="top")

```

Korrelation zwischen Anzahl an Doppelbindungen und Konzentration der FA

Laden der Metadaten

```{r}
FA_stool.db <- read.table("/Users/student05/Documents/DB sättigung .txt", sep = '\t', comment='',
                       head=T)
FA_stool.db$Time <- factor(FA_stool.db$Time, levels = c("PRE", "POST"))
```

Plotten der Korrelation
In Arbeit

```{r}
pdf("/Users/student05/Documents/fertige Plots/DB.2Konzentration.pdf",width=8, height=10)
ggscatter(FA_stool.db, x='DB', y='Concentration',color = "grey59",fill = "lightgray",shape = 19,  add = 'reg.line', conf.int = T, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, 3000), cor.coef.size = 5, xlab= 'Anzahl an Doppelbindungen', ylab = 'Konzentration [nmol/g]')+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")+
  geom_point(color='black', size=2.5)
dev.off()

cor.test(subset(filter(FA_stool.db))$DB, subset(filter(FA_stool.db))$Concentration, method = "spearman", exact = F)

p.adjust(c(2.2e-16), method = 'BH', n=1)

```
(S = 3684500, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
 rho 
-0.7034807)


2.2 Korrelationsanalysen zwischen den FAs mit Hilfe einer Korrelationsmatrix

Laden der Metadaten

```{r}
FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
                       head=T)
      FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST"))

FA_stool <- FA_stool[-c(58:64),]
FA_stool <- FA_

FA_stool<- add_rownames(FA_stool, "SampleID1")
FA_stool.r<- add_rownames(FA_stool, "SampleID1")


row.names(FA_stool) <- FA_stool$SampleID                 
```

Filtern nach PRE und POST
Hinzufügen von Spearman

```{r}
FA_stool_matrix_PRE <- subset(filter(FA_stool, Time == "PRE"))[ ,8:19]
FA_stool_matrix_POST <- subset(filter(FA_stool, Time == "POST"))[ ,8:19]

res.PRE <- cor(FA_stool_matrix_PRE)
res.POST <- cor(FA_stool_matrix_POST)

res2.PRE <- rcorr(as.matrix(FA_stool_matrix_PRE), type = "spearman")


res2.POST <- rcorr(as.matrix(FA_stool_matrix_POST), type = "spearman")
```
 Bestimmung des Korrelationskoeffizienten
 
```{r}
res2.PRE$r
res2.POST$r

FA_stool_PRE_CC <- as.matrix((res2.PRE$r))
FA_stool_POST_CC <- as.matrix(res2.POST$r)
```
 
Bestimmung p-values

```{r}
res2$P

FA_stool_PRE_PV <- as.matrix(res2.PRE$P)
FA_stool_POST_PV <- as.matrix(res2.POST$P)
```

Erstellen einer flattenCorrMatrix für PRE und POST

```{r}
flattenCorrMatrix.PRE <- function(FA_stool_PRE_CC, FA_stool_PRE_PV) {
  ut <- upper.tri(FA_stool_PRE_CC)
  data.frame(
    row = rownames(FA_stool_PRE_CC)[row(FA_stool_PRE_CC)[ut]],
    column = rownames(FA_stool_PRE_CC)[col(FA_stool_PRE_CC)[ut]],
    cor  =(FA_stool_PRE_CC)[ut],
    p = FA_stool_PRE_PV[ut]
  )
}

flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P)


flattenCorrMatrix.POST <- function(FA_stool_POST_CC, FA_stool_POST_PV) {
  ut <- upper.tri(FA_stool_POST_CC)
  data.frame(
    row = rownames(FA_stool_POST_CC)[row(FA_stool_POST_CC)[ut]],
    column = rownames(FA_stool_POST_CC)[col(FA_stool_POST_CC)[ut]],
    cor  =(FA_stool_POST_CC)[ut],
    p = FA_stool_POST_PV[ut]
  )
}

flattenCorrMatrix.POST(res2.POST$r, res2.POST$P)
```

Dataframe erstellen und Spalten umbenennen

```{r}
FA_PRE_cor.p <- as.data.frame(flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P))
FA_POST_cor.p <- as.data.frame(flattenCorrMatrix.POST(res2.POST$r, res2.POST$P))


colnames(FA_PRE_cor.p) <- c("FA", "FA", "correlation coefficient", "p-value")

colnames(FA_POST_cor.p) <- c("FA", "FA", "correlation coefficient", "p-value")
```

Correlogram erstellen
```{r}
corrplot(res.PRE, type = "upper", order = "hclust", 
         tl.col = "black", tl.srt = 45)


corrplot(res.POST, type = "upper", order = "hclust", 
         tl.col = "black", tl.srt = 45)


corrplot(res2.PRE$r, type="upper", order="hclust", 
         p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")

corrplot(res2.PRE$r, type="upper", order="hclust", 
         p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")

corrplot(res2.POST$r, type="upper", order="hclust", 
         p.mat = res2.POST$P, sig.level = 0.05, insig = "blank")

corrplot(res2.POST$r, type="upper", order="hclust", p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
  
```
Scatter Plot erstellen und Daten sichern

```{r}
chart.Correlation(FA_stool_matrix_PRE, histogram=TRUE, pch=19)
chart.Correlation(FA_stool_matrix_POST, histogram = T, pch = 19)       
         

write.table(FA_POST_cor.p, file ='/Users/student05/Documents/fa feces/FA fecal/correlations/FA post correlations cor p',sep ="\t", col.names = TRUE, row.names = FALSE)
write.table(FA_PRE_cor.p, file ='/Users/student05/Documents/fa feces/FA fecal/correlations/FA pre correlations cor p',sep ="\t", col.names = TRUE, row.names = FALSE)

```

2.3 Sterolkonvertierungstypen-Analyse der FA

Laden und filtern der Metadaten


```{r}
FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
                       head=T)

FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))

FA_stool <- FA_stool[-c(65, 66), ]

row.names(FA_stool) <- FA_stool$SampleID

FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))

FA_stool[1,4]<- "PRE"

FA_stool_pairs <- filter(FA_stool, Proband == "05AP" | Proband == "06WT"
                              
                              | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                              
                              | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                              
                              | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                              
                              | Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
                              
                              | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                              
                              | Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
                              
                              | Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
                              
                              | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")


FA_stool_pairs_PP <- filter(FA_stool_pairs, Time=="PRE" | Time=="POST")

FA_stool_pairs_PPFU <- filter(FA_stool, Proband == "05AP" | Proband == "13
                                   
                                   BS" | Proband == "17SK" | Proband == "22WS" | Proband ==
                                     
                                     "40WA" | Proband == "41ML" | Proband == "54SL")

```

In high und low Converter unterteilen

```{r}
lowconv <- filter(FA_stool, Proband == "05AP" | Proband == "33MP"
                  
                  | Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
                  
                  | Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")

lowconv['Phenotype'] = 'low converter'

highconv <- filter(FA_stool, Proband == "06WT" | Proband == "07RW"
                   
                   | Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
                   
                   | Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
                   
                   | Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
                   
                   | Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
                   
                   | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")

highconv['Phenotype'] = 'high converter'

highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL

noconv <- filter(FA_stool, Proband == "28HM" | Proband == "32FG"
                 
                 | Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
                 
                 | Proband == "39DA" | Proband == "66DG" | Proband == "70PL")

noconv['Phenotype'] = 'not classified'

noconv$Converter.Type <- NULL

convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)

convT_paired <- filter(convT, Proband == "05AP" | Proband == "06WT"
                       
                       | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                       
                       | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                       
                       | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                       
                       | Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
                       
                       | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                       
                       | Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
                       
                       | Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
                       
                       | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")

convT_paired_PP <- filter(convT_paired, Time=="PRE" | Time=="POST")

convT_paired_PPnc <- filter(subset(convT_paired_PP, !Phenotype == "not classified" ))

convT_paired_PPnc.PRE <- filter(subset(convT_paired_PPnc, Time =="PRE"))

convT_paired_PPnc.POST <- filter(subset(convT_paired_PPnc, Time =="POST"))

write.table(convT, file = '/Users/student05/Documents/fa feces/FA sterol converter types ', sep = "\t", col.names = TRUE,row.names = FALSE)
```



In high und low Converter unterteilen


```{r}
lowconv <- filter(FA_stool, Proband == "05AP" | Proband == "33MP"
                  
                  | Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
                  
                  | Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")

lowconv['Phenotype'] = 'low converter'

highconv <- filter(FA_stool, Proband == "06WT" | Proband == "07RW"
                   
                   | Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
                   
                   | Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
                   
                   | Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
                   
                   | Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
                   
                   | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")

highconv['Phenotype'] = 'high converter'

highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL

noconv <- filter(FA_stool, Proband == "28HM" | Proband == "32FG"
                 
                 | Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
                 
                 | Proband == "39DA" | Proband == "66DG" | Proband == "70PL")

noconv['Phenotype'] = 'not classified'

noconv$Converter.Type <- NULL

convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)

convT_paired <- filter(convT, Proband == "05AP" | Proband == "06WT"
                       
                       | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                       
                       | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                       
                       | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                       
                       | Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
                       
                       | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                       
                       | Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
                       
                       | Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
                       
                       | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")

convT_paired_PP <- filter(convT_paired, Time=="PRE" | Time=="POST")

convT_paired_PPnc <- filter(subset(convT_paired_PP, !Phenotype == "not classified" ))

convT_paired_PPnc.PRE <- filter(subset(convT_paired_PPnc, Time =="PRE"))

convT_paired_PPnc.POST <- filter(subset(convT_paired_PPnc, Time =="POST"))

write.table(convT, file = '/Users/student05/Documents/fa feces/FA sterol converter types ', sep = "\t", col.names = TRUE,row.names = FALSE)
```

Boxplot aller FA je nach Sterolkonvertierungstyp und Zeitpunkt
Melt Datenset
Alle FA

```{r}

FA_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype', 'Time'), measure.vars = c('sat', 'mono.unsat', 'di.unsat', 'more.2.unsat', 'less.14', 'c14.17', 'c18.19', 'c20.21', 'c22.24', 'total'))
FA_stool.melt <- subset(filter(FA_stool.melt, !Phenotype == "not classified"))

FA_stool.melt <- rename(FA_stool.melt, FA=variable)
FA_stool.melt <- rename(FA_stool.melt, Concentration=value)

  ggplot(FA_stool.melt,aes(x=Phenotype, y=Concentration, fill= FA)) +
  xlab ('Converter type') + ylab ('Concentration [nmol/g DW]') + 
  geom_boxplot()+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  facet_grid(.~Time)+ 
  scale_fill_manual(labels = c("saturated", "monounsaturated", "diunsaturated", "> 2 unsaturated", "< c14", "c 14-17", "c 18-19", "c 20-21", "c 22-24", "total"), 
                    values = c("tomato", "yellowgreen", "steelblue2", "orchid2", "deeppink", "brown4", "darkorange1", "blueviolet", "aquamarine3", "darksalmon"))
+theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))
```

Gesaettigte FA + wilcoxon test
in Arbeit

```{r}
sat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('sat'))
sat_stool.melt <- rename(sat_stool.melt, FA=variable)
sat_stool.melt <- rename(sat_stool.melt, Concentration=value)


ggplot(filter(sat_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [nmol/g]') + 
  scale_fill_manual(labels=c("Saturated fatty acids"), values = c("yellowgreen"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(filter(sat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') + 
  scale_fill_manual(labels=c("saturated fatty acid"), values = c("yellowgreen"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

mean(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "high converter"))$sat) 

sd(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "high converter"))$sat) 

mean(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "high converter"))$sat) 

sd(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "high converter"))$sat) 


mean(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "low converter"))$sat) 

sd(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "low converter"))$sat) 

mean(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "low converter"))$sat) 

sd(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "low converter"))$sat) 


pairwise.wilcox.test(subset(filter(convT_paired_PP, Time == "PRE"))$sat, subset(filter(convT_paired_PP, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(convT_paired_PP, Time == "POST"))$sat, subset(filter(convT_paired_PP, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(convT_paired_PP, Phenotype == "low converter"))$sat, subset(filter(convT_paired_PP, Phenotype == "low converter"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(convT_paired_PP, Phenotype == "high converter"))$sat, subset(filter(convT_paired_PP, Phenotype == "high converter"))$Time, p.adjust.method = 'BH', paired = F)


sat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('sat'))
sat_stool.melt$Time <-factor(sat_stool.melt$Time, levels = c("PRE", "POST"))
sat_stool.melt <- dplyr::rename(sat_stool.melt, FA=variable)
sat_stool.melt <- dplyr::rename(sat_stool.melt, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/converter.sat.pdf",width=8, height=10)
ggplot(filter(sat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= Phenotype)) +facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Gesättigte Fettsäurenkonzentration [nmol/g] ') + 
  scale_fill_manual(labels=c("high converter", "low converter"), values = c("seashell4", "seashell2"))+
  geom_boxplot(width = .7, lwd=0.6) + theme_classic() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("low converter")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")
dev.off()

```

Total FA

```{r}
total_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('total'))
total_stool.melt <- rename(total_stool.melt, FA=variable)
total_stool.melt <- rename(total_stool.melt, Concentration=value)


ggplot(filter(total_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [nmol/g]') + 
  scale_fill_manual(labels=c("total fatty acids"), values = c("steelblue2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

ggplot(filter(total_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') + 
  scale_fill_manual(labels=c("total fatty acid"), values = c("steelblue2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))
```

einfach ungesaettigt

```{r}
mono.unsat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('mono.unsat'))
mono.unsat_stool.melt <- rename(mono.unsat_stool.melt, FA=variable)
mono.unsat_stool.melt <- rename(mono.unsat_stool.melt, Concentration=value)


ggplot(filter(mono.unsat_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [nmol/g]') + 
  scale_fill_manual(labels=c("mono unsaturated fatt acids"), values = c("coral2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

ggplot(filter(mono.unsat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') + 
  scale_fill_manual(labels=c("mono unsaturated fatty acid"), values = c("coral2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))

```

zweifach ungesaettigt + wilcoxon test
in Arbeit

```{r}
di.unsat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('di.unsat'))
di.unsat_stool.melt <- rename(di.unsat_stool.melt, FA=variable)
di.unsat_stool.melt <- rename(di.unsat_stool.melt, Concentration=value)


ggplot(filter(di.unsat_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [nmol/g]') + 
  scale_fill_manual(labels=c("diunsaturated fatty acid"), values = c("seashell4"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))


mean(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "high converter"))$di.unsat) 

sd(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "high converter"))$di.unsat) 

mean(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "high converter"))$di.unsat) 

sd(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "high converter"))$di.unsat) 


mean(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "low converter"))$di.unsat) 

sd(subset(filter(convT_paired_PP, Time == "PRE" & Phenotype == "low converter"))$di.unsat) 

mean(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "low converter"))$di.unsat) 

sd(subset(filter(convT_paired_PP, Time == "POST" & Phenotype == "low converter"))$di.unsat) 


pairwise.wilcox.test(subset(filter(convT_paired_PP, Time == "PRE"))$di.unsat, subset(filter(convT_paired_PP, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(convT_paired_PP, Time == "POST"))$di.unsat, subset(filter(convT_paired_PP, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(convT_paired_PP, Phenotype == "low converter"))$di.unsat, subset(filter(convT_paired_PP, Phenotype == "low converter"))$Time, p.adjust.method = 'BH', paired = F)


pairwise.wilcox.test(subset(filter(convT_paired_PP, Phenotype == "high converter"))$di.unsat, subset(filter(convT_paired_PP, Phenotype == "high converter"))$Time, p.adjust.method = 'BH', paired = F)

pdf("/Users/student05/Documents/fertige Plots/converter.unsat.pdf",width=8, height=10)
ggplot(filter(di.unsat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= Phenotype)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Zweifach ungesättigte Fettsäurenkonzentration [nmol/g] ') + 
  scale_fill_manual(labels=c("high converter", "low converter"), values = c("seashell4", "seashell2"))+
  geom_boxplot(width = .7, lwd=0.6) + theme_classic() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")
dev.off()

```

Omega6/Omega3-ratio

```{r}
sat_stool.omega <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('ratio'))

sat_stool.omega$Time <-factor(sat_stool.omega$Time, levels = c("PRE", "POST"))

sat_stool.omega <- dplyr::rename(sat_stool.omega, FA=variable)
sat_stool.omega <- dplyr::rename(sat_stool.omega, Concentration=value)


ggplot(filter(sat_stool.omega, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= Phenotype)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Gesättigte Fettsäurenkonzentration [nmol/g] ') + 
  scale_fill_manual(labels=c("high converter", "low converter"), values = c("seashell4", "seashell2"))+
  geom_boxplot(width = .7, lwd=0.6) + theme_classic() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("low converter", "high converter")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")
```

mehr als zweifach ungesaettigt

```{r}
more.2.unsat_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('more.2.unsat'))

more.2.unsat_stool.melt <- rename(more.2.unsat_stool.melt, FA=variable)
more.2.unsat_stool.melt <- rename(more.2.unsat_stool.melt, Concentration=value)


ggplot(filter(more.2.unsat_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [nmol/g]') + 
  scale_fill_manual(labels=c("> 2 unsaturated"), values = c("brown4"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

ggplot(filter(more.2.unsat_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') + 
  scale_fill_manual(labels=c("> 2 unsaturated"), values = c("brown4"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))

```
C18

```{r}
c18.19_stool.melt <- melt(convT_paired_PP, id.vars = c('Phenotype','Time'), measure.vars = c('c18.19'))

c18.19_stool.melt <- rename(c18.19_stool.melt, FA=variable)
c18.19_stool.melt <- rename(c18.19_stool.melt, Concentration=value)


ggplot(filter(c18.19_stool.melt, !Time=="FOLLOW-UP" & !Phenotype=="not classified"),aes(x=Time, y=Concentration, fill= FA)) +
  facet_grid(.~ Phenotype) +
  xlab ('Time Point')+ ylab ('Concentration [nmol/g]') + 
  scale_fill_manual(labels=c("18-19 c-atomes"), values = c("yellow2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))

ggplot(filter(c18.19_stool.melt, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= FA)) +
  facet_grid(.~ Time) +
  xlab ('Converter type')+ ylab ('Concentration [nmol/g] ') + 
  scale_fill_manual(labels=c("18-19 c-atomes"), values = c("yellow2"))+
  geom_boxplot() +
  stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))

```

2.3 Alpha-Diversitaet und FAs
Shannon und Simpson

Laden und filtern der Metadaten

```{r}
map_alphadiv <- read.table("/Users/student05/Downloads/means_alpha_div.txt", sep = '\t', comment='',head = TRUE, row.names = 1)

FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
                       head=T)

View(FA_stool)

FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))

FA_stool <- FA_stool[-c(58:64), ]

row.names(FA_stool) <- FA_stool$SampleID

```

Synchonisieren der Datensets

```{r}
common.ids.St <- intersect(rownames(FA_stool), rownames(map_alphadiv))
common.ids.St <- intersect(row.names(FA_stool), row.names(map_alphadiv))

FA_stool <- FA_stool[common.ids.St,]

map_alphadiv <- map_alphadiv[common.ids.St,]

FA_stool$Shannon <- map_alphadiv$Shannon

FA_stool$Simpson <- map_alphadiv$Simpson

```

Loop Korrelation Shannon und FAs

```{r}
corr_colnames_FA <-colnames(FA_stool[,7:18])

corr_spearman_Shannon_FA <- data.frame()

for( i in unique(corr_colnames_FA)) {

  tmp <- filter(FA_stool, !is.na(i))

  x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))

  y = t(as.matrix(tmp$Shannon) )

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
  
  w = t(as.matrix(subset(filter(tmp, Time == "PRE"))$Shannon))
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")

  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
  
  s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Shannon))
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Shannon_FA)+1
 
  corr_spearman_Shannon_FA[nrow,"Div"] = "Shannon"
  
  corr_spearman_Shannon_FA[nrow, "column"] = i
  
  corr_spearman_Shannon_FA[nrow, "rho"] = rho
  
  corr_spearman_Shannon_FA[nrow, "p.value"] = p
  
  corr_spearman_Shannon_FA[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Shannon_FA[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Shannon_FA[nrow, "rho_POST"] = rho_POST
  
  corr_spearman_Shannon_FA[nrow, "p.value_POST"] = p_POST
  
 
}

corr_spearman_Shannon_FA$p.adjusted <- p.adjust(corr_spearman_Shannon_FA$p.value,method = "BH", n = 12)

corr_spearman_Shannon_FA$p.adjusted_PRE <-p.adjust(corr_spearman_Shannon_FA$p.value_PRE, method = "BH", n = 12)

corr_spearman_Shannon_FA$p.adjusted_POST <- p.adjust(corr_spearman_Shannon_FA$p.value_POST, method = "BH", n = 12)

write.table(corr_spearman_Shannon_FA, file = '/Users/student05/Documents/fa feces/tabellen/Shannon.FA.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Shannon und Fas die interessante Korrelation zeigen 

```{r}
ggplot(FA_stool, aes(x=total, y=Shannon)) + geom_point(aes(color=Time)) +
  scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Total fatty acid Concentration [nmol/g]') +
  ylab('Shannon-Index')

ggscatter(FA_stool, x='total', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time, scales = "free_x")+
  theme(legend.position="none")

ggscatter(FA_stool, x='sat', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time)

ggscatter(FA_stool, x='anteiso', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Anteiso fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time)

ggscatter(FA_stool, x='c22.24', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 22-24 fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time)

ggscatter(FA_stool, x='c20.21', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-21 fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(FA_stool, x='c14.17', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time)

ggscatter(FA_stool, x='less.14', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '< 14 c-atoms fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time)

ggscatter(FA_stool, x='more.2.unsat', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time)

ggscatter(FA_stool, x='di.unsat', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Diunsaturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index') +
  facet_wrap(~Time)

ggscatter(FA_stool, x='sat', y='Shannon',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')

ggscatter(FA_stool, x='di.unsat', y='Shannon',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Diunsaturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')


ggscatter(FA_stool, x='more.2.unsat', y='Shannon',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')


ggscatter(FA_stool, x='c20.21', y='Shannon',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c20-21 c atoms fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')

ggscatter(FA_stool, x='c22.24', y='Shannon',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c22-24 c atoms fatty acid Concentration [nmol/g]', ylab = 'Shannon-Index')


```

in der Arbeit
Total FA

```{r}
pdf("/Users/student05/Documents/fertige Plots/Shannon.totalFA.pdf",width=8, height=10)
ggscatter(FA_stool, x='total', y='Shannon',  add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0,7), cor.coef.size = 8, xlab= 'Gesamte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Shannon-Index')+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text( hjust=1))+
  geom_point(color='black', size=2.5)
dev.off()

```

Korrelation zwischen Shannon und Kettenlaenge
Laden der Metadaten

```{r}
FA_stool.sh <- read.table("/Users/student05/Documents/DB shannon.txt", sep = '\t', comment='',
                          head=T)
FA_stool_sh_pr <- subset(filter(FA_stool.sh, !Time =="POST"))
FA_stool_sh_po <- subset(filter(FA_stool.sh, !Time =="PRE"))
```

Plotten der Daten

```{r}
FA_stool_sh_pr$Shannon <- as.discrete(FA_stool_sh_pr$Shannon)
FA_stool_sh_pr$Concentration <- as.discrete(FA_stool_sh_pr$Concentration)

ggscatter(FA_stool_sh_pr, x='Concentration', y='Shannon',color = 'DB', palette = c('tomato', 'yellowgreen','steelblue'),  add = 'reg.line', conf.int = T, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Concentration [nmol/g]', ylab = 'Shannon Index')+
  facet_grid(.~ DB, scales="free")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(FA_stool_sh_po, x='Concentration', y='Shannon',color = 'DB',  add = 'reg.line', conf.int = T, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(250, 7),xlab= 'Concentration [nmol/g]', ylab = 'Shannon Index')+
  facet_grid(.~ DB, scales="free")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))
```

Loop Simpson und alle FAs

```{r}
corr_spearman_Simpson_FA <- data.frame()

for( i in unique(corr_colnames_FA)) {
  
  tmp <- filter(FA_stool, !is.na(i))

  x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))

  y = t(as.matrix(tmp$Simpson))

  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
  
  w = t(as.matrix (subset(filter(tmp, Time == "PRE"))$Simpson))
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")

  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
  
  s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Simpson))
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Simpson_FA)+1

  corr_spearman_Simpson_FA[nrow,"Div"] = "Simpson"
  
  corr_spearman_Simpson_FA[nrow, "column"] = i
  
  corr_spearman_Simpson_FA[nrow, "rho"] = rho
  
  corr_spearman_Simpson_FA[nrow, "p.value"] = p
  
  corr_spearman_Simpson_FA[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Simpson_FA[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Simpson_FA[nrow, "rho_POST"] = rho_POST
  
  corr_spearman_Simpson_FA[nrow, "p.value_POST"] = p_POST
  
}


corr_spearman_Simpson_FA$p.adjusted <- p.adjust(corr_spearman_Simpson_FA$p.value,method = "BH", n = 12)

corr_spearman_Simpson_FA$p.adjusted_PRE <-p.adjust(corr_spearman_Simpson_FA$p.value_PRE, method = "BH", n = 12)

corr_spearman_Simpson_FA$p.adjusted_POST <- p.adjust(corr_spearman_Simpson_FA$p.value_POST, method = "BH", n = 12)

write.table(corr_spearman_Simpson_FA, file = '/Users/student05/Documents/fa feces/tabellen/Simpson.FA.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


corr_sig_Simpson_FA <- filter(corr_spearman_Simpson_FA, p.adjusted < 0.05 | p.adjusted_PRE < 0.5 | p.adjusted_POST < 0.5 | p.adjusted_FU < 0.5)

```

Plotten der Metadaten Simpson und interessante FAs

```{r}
ggscatter(FA_stool, x='total', y='Shannon', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'),
          add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
          cor.method = 'spearman', xlab= 'Saturated fatty acids Concentration [nmol/g]', ylab = 'Simpson-Index') + 
  facet_wrap(~Time, scales = "free_x")

ggscatter(FA_stool, x='total', y='Simpson', color = 'Time', palette = c('yellowgreen', 'coral2', 'steelblue2'),
          add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
          cor.method = 'spearman', xlab= 'Total fatty acids Concentration [nmol/g]', ylab = 'Simpson-Index') + 
  facet_wrap(~Time)

ggscatter(FA_stool, x='sat', y='Simpson',
          add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
          cor.method = 'spearman', xlab= 'Saturated fatty acids Concentration [nmol/g]', ylab = 'Simpson-Index') 

ggscatter(FA_stool, x='total', y='Simpson',
          add = 'reg.line', conf.int = TRUE, cor.coef= TRUE, cor.coef.coord = c(0,0.998),
          cor.method = 'spearman', xlab= 'Total fatty acids Concentration [nmol/g]', ylab = 'Simpson-Index') 
 

```

2.4 Korrelationsanalysen zwischen Taxa und den FAs

Laden und filtern der Metadaten, Pylum und Genus level setzen, Daten sichern

```{r}
relab_means <- read.table('/Users/student05/Documents/relative abundance/relab_means_per_timepoint.txt', sep ='\t', comment='', head=T)


relab_means_melt <- melt(relab_means, id=c('Proband', 'Time'))

relab_means_melt <- dplyr::rename(relab_means_melt, Taxa=variable)

relab_means_melt <- dplyr::rename(relab_means_melt, Relative_Abundance=value)

relab_phylum <- subset(relab_means_melt, !grepl("g__|f__|o__|c__", relab_means_melt$Taxa))

relab_phylum <- subset(relab_phylum, !grepl("k__Archaea", relab_phylum$Taxa))

relab_phylum$Time <- factor(relab_phylum$Time, levels=c('PRE','POST','FOLLOW-UP'))

relab_phylum_spread <- spread(relab_phylum, Taxa, Relative_Abundance, sep = NULL)

relab_genus <- subset(relab_means_melt, grepl("g__", relab_means_melt$Taxa))

relab_genus <- subset(relab_genus, !grepl("k__Archaea", relab_genus$Taxa))

relab_genus$Time <- factor(relab_genus$Time, levels = c('PRE','POST','FOLLOW-UP'))

relab_genus_spread <- spread(relab_genus, Taxa, Relative_Abundance, sep = NULL)

write.table(relab_phylum_spread, file = '/Users/student05/Documents/relative abundance/relab_phylum.txt', sep= "\t", col.names = TRUE, row.names = FALSE)

write.table(relab_genus_spread, file = '/Users/student05/Documents/relative abundance/relab_genus.txt', sep ="\t", col.names = TRUE, row.names = FALSE)

relab_phylum_spread <- read.table("/Users/student05/Documents/relative abundance/relab_phylum.txt", sep = '\t', comment='',
                                  head=T)
relab_genus_spread <-  read.table("/Users/student05/Documents/relative abundance/relab_genus.txt", sep = '\t', comment='',
                                  head=T)
```

Synchonisieren der Metadatensets und Laden der FA-Metadaten

```{r}
relab_phylum_ID <- relab_phylum_spread

relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))

row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID

relab_genus_ID <- relab_genus_spread

relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))

row.names(relab_genus_ID) <- relab_genus_ID$SampleID

FA_stool <- read.table("/Users/student05/Documents/fa feces/Fa.feces.2.txt", sep = '\t', comment='',
                       head=T)

FA_stool$Time <-factor(FA_stool$Time, levels = c("PRE", "POST", "FOLLOW-UP"))


FA_stool <- subset(filter(FA_stool, !Time == "FOLLOW-UP"))

FA_stool <- subset(filter(FA_stool, !Proband == "31KE", !Proband == "34WF",
                          
                          !Proband == "45GL", !Proband == "49RJ", !Proband == "54SL", !Proband == "74SA"))

FA_stool <- mutate(FA_stool, SampleID1 = paste(Proband, Time, sep = "."))

row.names(FA_stool) <- FA_stool$SampleID

common.ids.relab <- intersect(rownames(FA_stool), rownames(relab_phylum_ID))

FA_stool <- FA_stool[common.ids.relab,]

relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]

write.table(FA_stool, file = '/Users/student05/Documents/fa feces/FA fecal/relative abundance/FA_stool_total.txt', sep= "\t", col.names = TRUE, row.names = FALSE)


```

Erstellen des Phylum-Datensets und hinzufuegen des Log und Pseudocounts 0.00001

```{r}
phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])

relab_phylum_ID <- relab_phylum_ID[,c(3:8)]+ 0.00001

relab_phylum_ID_log <- log10(relab_phylum_ID_log)

phylum_FA <- cbind(relab_phylum_ID, FA_stool[, c(1:19)])
phylum_FA$Time <- factor(phylum_FA$Time, levels = c("PRE", "POST"))

```

Loop fuer Korrelation zwischen gesaettigten FA und Phylum-Level

```{r}
corr_map_phylum_sat <- filter(phylum_FA, !is.na(sat))

corr_spearman_Phylum_sat <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_sat, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$sat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$sat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$sat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_sat)+1
  
  corr_spearman_Phylum_sat[nrow,"FA"] <- "sat"
  
  corr_spearman_Phylum_sat[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_sat[nrow, "p.value"] = p
  
  corr_spearman_Phylum_sat[nrow, "rho"] = rho
  
  corr_spearman_Phylum_sat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_sat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_sat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_sat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_sat$p.adjusted <- p.adjust(corr_spearman_Phylum_sat$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_sat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_sat$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_sat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_sat$p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_sat <- filter(corr_spearman_Phylum_sat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_sat, file = '/Users/student05/Documents/fa feces/tabellen/sat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten der gesaettigten FA und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time, scales="free_x")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Proteobacteria')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Proteobacteria', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -1.7), cor.coef.size = 6, xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Proteobacteria')+
  
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=0, hjust=1))

ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(250, -1), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Firmicutes',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(250, -0.75), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Firmicutes')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

stat_cor(method = "pearson", label.x = 3, label.y = 30)

ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid  Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid  Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)

```

in Arbeit

```{r}
pdf("/Users/student05/Documents/fertige Plots/sat.verru.pdf",width=8, height=10)
ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = '',cor.coef.size = 8,xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen p__Verrucomicrobia [%]')+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  geom_point(color='black', size=2.5)+
  scale_y_log10(labels = percent_format())
  dev.off()
  
  pdf("/Users/student05/Documents/fertige Plots/sat.bacteroidetes.pdf",width=8, height=10)
ggscatter(phylum_FA, x='sat', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', size = 2.5,conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(0, -0.8),cor.coef.size = 7, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen p__Bacteroidetes [%]')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  scale_y_log10(labels = percent_format())
dev.off()

pdf("/Users/student05/Documents/fertige Plots/unsat.bacteroidetes.pdf",width=8, height=10)
ggscatter(phylum_FA, x='unsat', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', size = 2.5,conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(0, -0.8),cor.coef.size = 7, xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen p__Bacteroidetes [%]')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  scale_y_log10(labels = percent_format())
dev.off()

pdf("/Users/student05/Documents/fertige Plots/omega3.bacteroidetes.neu.pdf",width=8, height=10)
ggscatter(phylum_FA, x='Omega3', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', size = 2.5,conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(0, -0.8),cor.coef.size = 7, xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen p__Bacteroidetes [%]')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  scale_y_log10(labels = percent_format())
dev.off()

pdf("/Users/student05/Documents/fertige Plots/sat.akkermansia.pdf",width=8, height=10)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.8),cor.coef.size = 8, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'log10 (Relatives Vorkommen g__Akkermansia)')+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")+
  geom_point(color='grey52')
dev.off()
```

Loop einfach ungesaettigte FA und Phylum-Level

```{r}
corr_map_phylum_mono.unsat <- filter(phylum_FA, !is.na(mono.unsat))

corr_spearman_Phylum_mono.unsat <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_mono.unsat, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$mono.unsat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$mono.unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$mono.unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_mono.unsat)+1

  corr_spearman_Phylum_mono.unsat[nrow,"FA"] <- "mono.unsat"
  
  corr_spearman_Phylum_mono.unsat[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_mono.unsat[nrow, "p.value"] = p
  
  corr_spearman_Phylum_mono.unsat[nrow, "rho"] = rho
  
  corr_spearman_Phylum_mono.unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_mono.unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_mono.unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_mono.unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_mono.unsat$p.adjusted <- p.adjust(corr_spearman_Phylum_mono.unsat$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_mono.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_mono.unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_mono.unsat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_mono.unsat$p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_mono.unsat <- filter(corr_spearman_Phylum_sat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_mono.unsat, file = '/Users/student05/Documents/fa feces/tabellen/mono.unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von einfach ungesaettigten FA und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Monounsaturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='mono.unsat', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Monounsaturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Proteobacteria')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Monounsaturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='mono.unsat', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Monounsaturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Actinobacteria')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop zweifach ungesaettigten FA und phylum-level

```{r}
corr_map_phylum_more.2.unsat <- filter(phylum_FA, !is.na(more.2.unsat))

corr_spearman_Phylum_more.2.unsat <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_more.2.unsat, !is.na(i))

  y = tmp[,i]
  
  x = tmp$more.2.unsat

  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$more.2.unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$more.2.unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_more.2.unsat)+1

  corr_spearman_Phylum_more.2.unsat[nrow,"FA"] <- "> 2 unsat"
  
  corr_spearman_Phylum_more.2.unsat[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_more.2.unsat[nrow, "p.value"] = p
  
  corr_spearman_Phylum_more.2.unsat[nrow, "rho"] = rho
  
  corr_spearman_Phylum_more.2.unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_more.2.unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_more.2.unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_more.2.unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_more.2.unsat$p.adjusted <- p.adjust(corr_spearman_Phylum_more.2.unsat$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_more.2.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_more.2.unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_more.2.unsat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_more.2.unsat$p.value_POST, method = "BH", n = 35)

corr_sig_Phylum_more.2.unsat <- filter(corr_spearman_Phylum_more.2.unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_more.2.unsat, file = '/Users/student05/Documents/fa feces/tabellen/more.2.unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von zweifach ungesaettigten FA und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Diunsaturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Diunsaturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  geom_text(aes(label=Proband),hjust=0, vjust=0)

ggscatter(phylum_FA, x='more.2.unsat', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='more.2.unsat', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Diunsaturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop weniger 14C und phylum-level

```{r}
corr_map_phylum_less.14 <- filter(phylum_FA, !is.na(less.14))

corr_spearman_Phylum_less.14 <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_less.14, !is.na(i))

  y = tmp[,i]
  
  x = tmp$less.14
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$less.14
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$less.14
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_less.14)+1

  corr_spearman_Phylum_less.14[nrow,"FA"] <- "< 14 c"
  
  corr_spearman_Phylum_less.14[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_less.14[nrow, "p.value"] = p
  
  corr_spearman_Phylum_less.14[nrow, "rho"] = rho
  
  corr_spearman_Phylum_less.14[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_less.14[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_less.14[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_less.14[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_less.14$p.adjusted <- p.adjust(corr_spearman_Phylum_less.14$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_less.14$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_less.14$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_less.14$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_less.14$p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_less.14 <- filter(corr_spearman_Phylum_less.14, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_less.14, file = '/Users/student05/Documents/fa feces/tabellen/less.14.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

 Plotten FA mit weniger als 14C atome und phylum-level
 
```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('< 14 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
geom_text(aes(label=Proband),hjust=0, vjust=0)

ggscatter(phylum_FA, x='less.14', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Less 14c fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Proteobacteria')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```
 
Loop C14-17 FA und phylum-level

```{r}
corr_map_phylum_c14.17 <- filter(phylum_FA, !is.na(c14.17 ))

corr_spearman_Phylum_c14.17  <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_c14.17 , !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$c14.17 

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$c14.17 
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$c14.17 
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_c14.17 )+1

  corr_spearman_Phylum_c14.17 [nrow,"FA"] <- "c 14-17"
  
  corr_spearman_Phylum_c14.17 [nrow, "Phylum"] = i
  
  corr_spearman_Phylum_c14.17 [nrow, "p.value"] = p
  
  corr_spearman_Phylum_c14.17 [nrow, "rho"] = rho
  
  corr_spearman_Phylum_c14.17 [nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_c14.17 [nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_c14.17 [nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_c14.17 [nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_c14.17 $p.adjusted <- p.adjust(corr_spearman_Phylum_c14.17 $p.value, method = "BH", n = 35) 

corr_spearman_Phylum_c14.17 $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_c14.17 $p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_c14.17 $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_c14.17 $p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_c14.17 <- filter(corr_spearman_Phylum_c14.17, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_c14.17, file = '/Users/student05/Documents/fa feces/tabellen/c14.17.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten C14-17 FA und phylum-level
 
```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('14-17 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='c14.17', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Proteobacteria')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('< 14-17 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('< 14-17 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('< 14-17 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='c14.17', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(150, -0.7), xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Firmicutes')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='c14.17', y='k__Bacteria.p__Firmicutes', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord =c(150, -0.7), xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Firmicutes')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")


```
 
 Loop C18 FA und phylum-level
 
```{r}
orr_map_phylum_c18.19 <- filter(phylum_FA, !is.na(c18 ))

corr_spearman_Phylum_c18.19  <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_c18.19 , !is.na(i))

  y = tmp[,i]
  
  x = tmp$c18
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$c18
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$c18
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_c18.19 )+1

  corr_spearman_Phylum_c18.19 [nrow,"FA"] <- "c 18-19"
  
  corr_spearman_Phylum_c18.19 [nrow, "Phylum"] = i
  
  corr_spearman_Phylum_c18.19 [nrow, "p.value"] = p
  
  corr_spearman_Phylum_c18.19 [nrow, "rho"] = rho
  
  corr_spearman_Phylum_c18.19 [nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_c18.19 [nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_c18.19 [nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_c18.19 [nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_c18.19 $p.adjusted <- p.adjust(corr_spearman_Phylum_c18.19 $p.value, method = "BH", n = 35) 

corr_spearman_Phylum_c18.19 $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_c18.19 $p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_c18.19 $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_c18.19 $p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_c18.19 <- filter(corr_spearman_Phylum_c18.19, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_c18.19, file = '/Users/student05/Documents/fa feces/tabellen/c18.19.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```
 
Plotten von C18 FA und phylum-level
 
```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('18-19 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('< 18-19 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('< 14-17 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(phylum_FA, x='c18.19', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 18-19 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Verrucomicrobia')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```
 
Loop C20-24 und phylum-level

```{r}
corr_map_phylum_c20.24 <- filter(phylum_FA, !is.na(c20.24 ))

corr_spearman_Phylum_c20.24  <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_c20.24 , !is.na(i))

  y = tmp[,i]
  
  x = tmp$c20.24 
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$c20.24 
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$c20.24 
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_c20.24 )+1

  corr_spearman_Phylum_c20.24 [nrow,"FA"] <- "c 20-24"
  
  corr_spearman_Phylum_c20.24 [nrow, "Phylum"] = i
  
  corr_spearman_Phylum_c20.24 [nrow, "p.value"] = p
  
  corr_spearman_Phylum_c20.24 [nrow, "rho"] = rho
  
  corr_spearman_Phylum_c20.24 [nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_c20.24 [nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_c20.24 [nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_c20.24 [nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_c20.24$p.adjusted <- p.adjust(corr_spearman_Phylum_c20.24$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_c20.24$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_c20.24$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_c20.24$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_c20.24$p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_c20.24 <- filter(corr_spearman_Phylum_c20.24, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_c20.24, file = '/Users/student05/Documents/fa feces/tabellen/c20.24.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von C20-24 und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-21 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('< 18-19 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-21 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='c20.21', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-21 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-21 c-atoms fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='c20.21', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-21 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Tenericutes')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop total FA und phylum-level

```{r}
corr_map_phylum_total <- filter(phylum_FA, !is.na(total ))

corr_spearman_Phylum_total  <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_total , !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$total 
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$total 
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$total 
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_total )+1
  
  corr_spearman_Phylum_total [nrow,"FA"] <- "total"
  
  corr_spearman_Phylum_total [nrow, "Phylum"] = i
  
  corr_spearman_Phylum_total [nrow, "p.value"] = p
  
  corr_spearman_Phylum_total [nrow, "rho"] = rho
  
  corr_spearman_Phylum_total [nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_total [nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_total [nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_total [nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_total $p.adjusted <- p.adjust(corr_spearman_Phylum_total $p.value, method = "BH", n = 35) 
corr_spearman_Phylum_total $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_total $p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_total $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_total $p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_total <- filter(corr_spearman_Phylum_total, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)


write.table(corr_spearman_Phylum_total, file = '/Users/student05/Documents/fa feces/tabellen/total.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von total FA und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='total', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance p__Proteobacteria')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='total', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Actinobacteria')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(phylum_FA, x='c20.21', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-21 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Tenericutes')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop Omega3 und phylum-level

```{r}
corr_map_phylum_Omega3 <- filter(phylum_FA, !is.na(Omega3 ))

corr_spearman_Phylum_Omega3  <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_Omega3 , !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Omega3
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Omega3
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Omega3 
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_Omega3 )+1
  
  corr_spearman_Phylum_Omega3 [nrow,"FA"] <- "Omega3"
  
  corr_spearman_Phylum_Omega3 [nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Omega3 [nrow, "p.value"] = p
  
  corr_spearman_Phylum_Omega3 [nrow, "rho"] = rho
  
  corr_spearman_Phylum_Omega3 [nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Omega3 [nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Omega3[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Omega3[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Omega3$p.adjusted <- p.adjust(corr_spearman_Phylum_Omega3$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_Omega3$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Omega3$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_Omega3$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Omega3$p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_Omega3<- filter(corr_spearman_Phylum_Omega3, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_Omega3, file = '/Users/student05/Documents/fa feces/tabellen/Omega3.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten Omega3 und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=Omega3)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=Omega3)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Omega3)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


pdf("/Users/student05/Documents/fertige Plots/Linolsäure.bacteroidetes.pdf",width=8, height=10)
ggscatter(phylum_FA, x='Omega3', y='k__Bacteria.p__Bacteroidetes',color = 'Time', label = 'Proband',palette = c('skyblue', 'orchid'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(0, -0.9), cor.coef.size = 7,xlab= 'Fäkale alpha-Linolensäure Konzentrationen [nmol/g]', ylab = 'log10 (Relatives Vorkommen p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")
dev.off()
```

Loop Omega 6 und Phylum-level

```{r}
corr_map_phylum_Omega6 <- filter(phylum_FA, !is.na(Omega6 ))

corr_spearman_Phylum_Omega6  <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_Omega6 , !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Omega6 
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Omega6 
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Omega6 
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_Omega6 )+1
 
  corr_spearman_Phylum_Omega6 [nrow,"FA"] <- "Omega6"
  
  corr_spearman_Phylum_Omega6 [nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Omega6 [nrow, "p.value"] = p
  
  corr_spearman_Phylum_Omega6 [nrow, "rho"] = rho
  
  corr_spearman_Phylum_Omega6 [nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Omega6 [nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Omega6 [nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Omega6 [nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Omega6 $p.adjusted <- p.adjust(corr_spearman_Phylum_Omega6 $p.value, method = "BH", n = 35) 
corr_spearman_Phylum_Omega6 $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Omega6 $p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_Omega6 $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Omega6 $p.value_POST, method = "BH", n = 35)

corr_sig_Phylum_Omega6 <- filter(corr_spearman_Phylum_Omega6, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_Omega6, file = '/Users/student05/Documents/fa feces/tabellen/Omega6.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


```

Plotten omega6 und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=Omega6)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=Omega6)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Omega6)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=Linolsaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Linolsaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

```

Loop omega6/omega3-ratio und phylum-level

```{r}
corr_map_phylum_ratio <- filter(phylum_FA, !is.na(ratio))

corr_spearman_Phylum_ratio  <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_ratio , !is.na(i))
  y = tmp[,i]
  
  x = tmp$ratio 
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$ratio 
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$ratio 
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_ratio )+1
  
  corr_spearman_Phylum_ratio [nrow,"FA"] <- "ratio"
  
  corr_spearman_Phylum_ratio [nrow, "Phylum"] = i
  
  corr_spearman_Phylum_ratio [nrow, "p.value"] = p
  
  corr_spearman_Phylum_ratio [nrow, "rho"] = rho
  
  corr_spearman_Phylum_ratio [nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_ratio [nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_ratio [nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_ratio [nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_ratio $p.adjusted <- p.adjust(corr_spearman_Phylum_ratio $p.value, method = "BH", n = 35) 

corr_spearman_Phylum_ratio $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_ratio $p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_ratio $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_ratio $p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_ratio <- filter(corr_spearman_Phylum_ratio, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)


write.table(corr_spearman_Phylum_ratio, file = '/Users/student05/Documents/fa feces/tabellen/ratio.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


```

Plotten von Omega6/omega3-ratio und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=ratio)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=ratio)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=ratio)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=ratio)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('EPA intake [g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=ratio)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=ratio)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('DHA intake [g]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")
```

Loop ungesaettigte FA und Phylum-level

```{r}

corr_map_phylum_unsat <- filter(phylum_FA, !is.na(unsat))

corr_spearman_Phylum_unsat  <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_unsat , !is.na(i))

  y = tmp[,i]
  
  x = tmp$unsat 

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$unsat 
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$unsat 
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_unsat )+1
  
  corr_spearman_Phylum_unsat [nrow,"FA"] <- "unsat"
  
  corr_spearman_Phylum_unsat [nrow, "Phylum"] = i
  
  corr_spearman_Phylum_unsat [nrow, "p.value"] = p
  
  corr_spearman_Phylum_unsat [nrow, "rho"] = rho
  
  corr_spearman_Phylum_unsat [nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_unsat [nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_unsat [nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_unsat [nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_unsat $p.adjusted <- p.adjust(corr_spearman_Phylum_unsat $p.value, method = "BH", n = 35) 

corr_spearman_Phylum_unsat $p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_unsat $p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_unsat $p.adjusted_POST <- p.adjust(corr_spearman_Phylum_unsat $p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_unsat <- filter(corr_spearman_Phylum_unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)


write.table(corr_spearman_Phylum_unsat, file = '/Users/student05/Documents/fa feces/tabellen/unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Analysen zwischen FA und Genus-level
Datensets filtern und hinzufuegen von log und pseudocount 0.00001

```{r}
genus_colnames <- colnames(relab_genus_spread[, c(3:31)])

relab_genus_ID <- relab_genus_ID[,c(3:31)] + 0.00001

relab_genus_ID_log <- log10(relab_genus_ID_log)

genus_FA <- cbind(relab_genus_ID, FA_stool[, c(1:19)])
genus_FA$Time <- factor(genus_FA$Time, levels = c("PRE", "POST"))
```

Loop gesaettigte FA und genus-level

```{r}
corr_map_genus_sat <- filter(genus_FA, !is.na(sat))

corr_spearman_genus_sat <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_sat, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$sat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$sat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$sat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_sat)+1
  
  corr_spearman_genus_sat[nrow,"FA"] = "saturated"
  
  corr_spearman_genus_sat[nrow, "Genus"] = i
  
  corr_spearman_genus_sat[nrow, "p.value"] = p
  
  corr_spearman_genus_sat[nrow, "rho"] = rho
  
  corr_spearman_genus_sat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_sat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_sat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_sat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_sat$p.adjusted <- p.adjust(corr_spearman_genus_sat$p.value, method = "BH", n = 35)

corr_spearman_genus_sat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_sat$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_sat$p.adjusted_POST <- p.adjust(corr_spearman_genus_sat$p.value_POST, method = "BH", n = 35)


corr_sig_genus_sat <- filter(corr_spearman_genus_sat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_sat, file = '/Users/student05/Documents/fa feces/tabellen/sat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

PLotten von gesaettigten FA und genus-level

```{r}
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(400, -1.3),cor.coef.size = 5, xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")


ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time)


ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")


ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated Concentration [nmol/mg]') + 
  ylab('log10 (Relative Abundance g__Collinsella)')+
  facet_wrap(~Time)

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab(' Concentration [mg/ml]') +  ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
  facet_wrap(~Time)

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acids Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)

ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance f__Rikenellaceae')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Faecalibacterium )')+
  facet_wrap(~Time)

ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=sat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Saturated fatt acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Akkermansia )')+
  facet_wrap(~Time)


ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(400, -0.5), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(400, -0.5), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
  
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(500, -1), xlab= 'Saturated fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

```
in Arbeit 

```{r}
pdf("/Users/student05/Documents/fertige Plots/sat.faecali.pdf",width=8, height=10)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -1.3),cor.coef.size = 8, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen g__Faecalibacterium [%]')+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")+
  geom_point(color='black', size=2.5)+
  scale_y_log10(labels = percent_format())
dev.off()

pdf("/Users/student05/Documents/fertige Plots/sat.oscillo.pdf",width=8, height=10)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -2),cor.coef.size = 8, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Relatives Vorkommen g__Oscillospira [%]')+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")+
  geom_point(color='black', size=2.5)+
  scale_y_log10(labels = percent_format())
dev.off()

pdf("/Users/student05/Documents/fertige Plots/sat.akkermansia.pdf",width=8, height=10)
ggscatter(genus_FA, x='sat', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',color = "grey59",fill = "lightgray", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, -0.7),cor.coef.size = 8, xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab ='Relatives Vorkommen g__Akkermansia [%]')+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")+
  geom_point(color='black', size=2.5)+
  scale_y_log10(labels = percent_format())
dev.off()
```

Loop einfach ungesaettigte FA und genus-level

```{r}
corr_map_genus_mono.unsat <- filter(genus_FA, !is.na(mono.unsat))

corr_spearman_genus_mono.unsat <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_mono.unsat, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$mono.unsat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$mono.unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$mono.unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_mono.unsat)+1
  
  corr_spearman_genus_mono.unsat[nrow,"FA"] = "mono.unsaturated"
  
  corr_spearman_genus_mono.unsat[nrow, "Genus"] = i
  
  corr_spearman_genus_mono.unsat[nrow, "p.value"] = p
  
  corr_spearman_genus_mono.unsat[nrow, "rho"] = rho
  
  corr_spearman_genus_mono.unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_mono.unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_mono.unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_mono.unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_mono.unsat$p.adjusted <- p.adjust(corr_spearman_genus_mono.unsat$p.value, method = "BH", n = 35)

corr_spearman_genus_mono.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_mono.unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_mono.unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_mono.unsat$p.value_POST, method = "BH", n = 35)



corr_sig_genus_mono.unsat <- filter(corr_spearman_genus_mono.unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_mono.unsat, file = '/Users/student05/Documents/fa feces/tabellen/mono.unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von einfach ungesaettigten FA und genus-level

```{r}
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time)

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bacteroides)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Sutterella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Prevotella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated Concentration [nmol/mg]') + 
  ylab('log10 (Relative Abundance g__Collinsella)')+
  facet_wrap(~Time)

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab(' Concentration [mg/ml]') + ylab('log10 (Relative Abundance f__Erysipelotrichaceae)')+
  facet_wrap(~Time)

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acids Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Faecalibacterium )')+
  facet_wrap(~Time)

ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=mono.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('mono.unsaturated fatt acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Akkermansia )')+
  facet_wrap(~Time)

```

Loop zweifach ungesaettigte FA und genus-level

```{r}
corr_map_genus_di.unsat <- filter(genus_FA, !is.na(di.unsat))

corr_spearman_genus_di.unsat <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_di.unsat, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$di.unsat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$di.unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$di.unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_di.unsat)+1
  
  corr_spearman_genus_di.unsat[nrow,"FA"] = "di.unsaturated"
  
  corr_spearman_genus_di.unsat[nrow, "Genus"] = i
  
  corr_spearman_genus_di.unsat[nrow, "p.value"] = p
  
  corr_spearman_genus_di.unsat[nrow, "rho"] = rho
  
  corr_spearman_genus_di.unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_di.unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_di.unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_di.unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_di.unsat$p.adjusted <- p.adjust(corr_spearman_genus_di.unsat$p.value, method = "BH", n = 35)

corr_spearman_genus_di.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_di.unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_di.unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_di.unsat$p.value_POST, method = "BH", n = 35)


corr_sig_genus_di.unsat <- filter(corr_spearman_genus_di.unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_di.unsat, file = '/Users/student05/Documents/fa feces/tabellen/di.unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```
PLotten von zweifach ungesaettigten FAs und genus-level

```{r}

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=di.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=di.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bacteroides)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=di.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Sutterella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, x=di.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Dorea)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister, x=di.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('di.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Dialister)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop mehr als zweifach ungesaettigte FAs und genus-level

```{r}
corr_map_genus_more.2.unsat <- filter(genus_FA, !is.na(more.2.unsat))

corr_spearman_genus_more.2.unsat <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_more.2.unsat, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$more.2.unsat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$more.2.unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$more.2.unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_more.2.unsat)+1
  
  corr_spearman_genus_more.2.unsat[nrow,"FA"] = "more.2.unsaturated"
  
  corr_spearman_genus_more.2.unsat[nrow, "Genus"] = i
  
  corr_spearman_genus_more.2.unsat[nrow, "p.value"] = p
  
  corr_spearman_genus_more.2.unsat[nrow, "rho"] = rho
  
  corr_spearman_genus_more.2.unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_more.2.unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_more.2.unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_more.2.unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_more.2.unsat$p.adjusted <- p.adjust(corr_spearman_genus_more.2.unsat$p.value, method = "BH", n = 35)

corr_spearman_genus_more.2.unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_more.2.unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_more.2.unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_more.2.unsat$p.value_POST, method = "BH", n = 35)


corr_sig_genus_more.2.unsat <- filter(corr_spearman_genus_more.2.unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_more.2.unsat, file = '/Users/student05/Documents/fa feces/tabellen/more.2.unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von mehr als zweifach ungesaettigten FA und genus-level

```{r}
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time) +
  theme(text = element_text(size=12),
                          axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bacteroides)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Sutterella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))+
geom_text(aes(label=Proband),hjust=0, vjust=0)

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Collinsella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister, x=more.2.unsat)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('more.2.unsaturated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Dialister)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```


Loop FA weniger als 14C und genus-level

```{r}
corr_map_genus_less.14 <- filter(genus_FA, !is.na(less.14))

corr_spearman_genus_less.14 <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_less.14, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$less.14
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$less.14
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$less.14
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_less.14)+1
  
  corr_spearman_genus_less.14[nrow,"FA"] = "less.14"
  
  corr_spearman_genus_less.14[nrow, "Genus"] = i
  
  corr_spearman_genus_less.14[nrow, "p.value"] = p
  
  corr_spearman_genus_less.14[nrow, "rho"] = rho
  
  corr_spearman_genus_less.14[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_less.14[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_less.14[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_less.14[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_less.14$p.adjusted <- p.adjust(corr_spearman_genus_less.14$p.value, method = "BH", n = 35)

corr_spearman_genus_less.14$p.adjusted_PRE <- p.adjust(corr_spearman_genus_less.14$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_less.14$p.adjusted_POST <- p.adjust(corr_spearman_genus_less.14$p.value_POST, method = "BH", n = 35)


corr_sig_genus_less.14 <- filter(corr_spearman_genus_less.14, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_less.14, file = '/Users/student05/Documents/fa feces/tabellen/less.14.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten weniger als 14C FA und genus-level

```{r}
ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('less.14urated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bacteroides)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('less.14urated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Sutterella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Collinsella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium., x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Eubacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))
  
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Prevotella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Faecalibacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium., x=less.14)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Eubacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop C14-17 FA und genus-level

```{r}
corr_map_genus_c14.17 <- filter(genus_FA, !is.na(c14.17))

corr_spearman_genus_c14.17 <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_c14.17, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$c14.17
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$c14.17
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$c14.17
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_c14.17)+1
  
  corr_spearman_genus_c14.17[nrow,"FA"] = "c14.17"
  
  corr_spearman_genus_c14.17[nrow, "Genus"] = i
  
  corr_spearman_genus_c14.17[nrow, "p.value"] = p
  
  corr_spearman_genus_c14.17[nrow, "rho"] = rho
  
  corr_spearman_genus_c14.17[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_c14.17[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_c14.17[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_c14.17[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_c14.17$p.adjusted <- p.adjust(corr_spearman_genus_c14.17$p.value, method = "BH", n = 35)

corr_spearman_genus_c14.17$p.adjusted_PRE <- p.adjust(corr_spearman_genus_c14.17$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_c14.17$p.adjusted_POST <- p.adjust(corr_spearman_genus_c14.17$p.value_POST, method = "BH", n = 35)


corr_sig_genus_c14.17 <- filter(corr_spearman_genus_c14.17, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_c14.17, file = '/Users/student05/Documents/fa feces/tabellen/c14.17.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von C14-17 FA und genus-level

```{r}
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 14-17 fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time) +
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 14-17 fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('> 14 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bacteroides)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c14.17urated fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Sutterella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab(' 14-17 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Collinsella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('14-17 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Akkermansia)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 14-17 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Faecalibacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('14-17 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

genus_FA$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__
ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__, x=c14.17)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('14-17 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Barnesiellaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='c14.17', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
 theme(legend.position="none")

ggscatter(genus_FA, x='c14.17', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 14-17 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
 theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


```

Loop C18 FA und genus-level

```{r}
corr_map_genus_c18 <- filter(genus_FA, !is.na(c18))

corr_spearman_genus_c18 <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_c18, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$c18
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$c18
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$c18
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_c18)+1
  
  corr_spearman_genus_c18[nrow,"FA"] = "c18"
  
  corr_spearman_genus_c18[nrow, "Genus"] = i
  
  corr_spearman_genus_c18[nrow, "p.value"] = p
  
  corr_spearman_genus_c18[nrow, "rho"] = rho
  
  corr_spearman_genus_c18[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_c18[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_c18[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_c18[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_c18$p.adjusted <- p.adjust(corr_spearman_genus_c18$p.value, method = "BH", n = 35)

corr_spearman_genus_c18$p.adjusted_PRE <- p.adjust(corr_spearman_genus_c18$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_c18$p.adjusted_POST <- p.adjust(corr_spearman_genus_c18$p.value_POST, method = "BH", n = 35)


corr_sig_genus_c18 <- filter(corr_spearman_genus_c18, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_c18, file = '/Users/student05/Documents/fa feces/tabellen/c18.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


```

Plotten C18 FA und genus-level

```{r}
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 18 fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time) +
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 18 fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bacteroides)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Sutterella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Ruminococcaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Akkermansia)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 18 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Faecalibacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__, x=c18.19)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('18 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Barnesiellaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop C20-24 FA und genus-level

```{r}
corr_map_genus_c20.24 <- filter(genus_FA, !is.na(c20.24))

corr_spearman_genus_c20.24 <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_c20.24, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$c20.24
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$c20.24
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$c20.24
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_c20.24)+1
  
  corr_spearman_genus_c20.24[nrow,"FA"] = "c20.24"
  
  corr_spearman_genus_c20.24[nrow, "Genus"] = i
  
  corr_spearman_genus_c20.24[nrow, "p.value"] = p
  
  corr_spearman_genus_c20.24[nrow, "rho"] = rho
  
  corr_spearman_genus_c20.24[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_c20.24[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_c20.24[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_c20.24[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_c20.24$p.adjusted <- p.adjust(corr_spearman_genus_c20.24$p.value, method = "BH", n = 35)

corr_spearman_genus_c20.24$p.adjusted_PRE <- p.adjust(corr_spearman_genus_c20.24$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_c20.24$p.adjusted_POST <- p.adjust(corr_spearman_genus_c20.24$p.value_POST, method = "BH", n = 35)


corr_sig_genus_c20.24 <- filter(corr_spearman_genus_c20.24, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_c20.24, file = '/Users/student05/Documents/fa feces/tabellen/c20.24.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von C20-24 FA und genus-level

```{r}
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 20-24 fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time) +
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='c20.21', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-24 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 20-24 fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bacteroides)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Sutterella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Ruminococcaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Akkermansia)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('c 20-24 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Faecalibacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='c20.21', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'c 20-24 fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__, x=c20.21)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('20-24 c-atoms fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Barnesiellaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop total FA und genus-level

```{r}
corr_map_genus_total <- filter(genus_FA, !is.na(total))

corr_spearman_genus_total <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_total, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$total
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$total
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$total
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_total)+1
  
  corr_spearman_genus_total[nrow,"FA"] = "total"
  
  corr_spearman_genus_total[nrow, "Genus"] = i
  
  corr_spearman_genus_total[nrow, "p.value"] = p
  
  corr_spearman_genus_total[nrow, "rho"] = rho
  
  corr_spearman_genus_total[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_total[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_total[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_total[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_total$p.adjusted <- p.adjust(corr_spearman_genus_total$p.value, method = "BH", n = 35)

corr_spearman_genus_total$p.adjusted_PRE <- p.adjust(corr_spearman_genus_total$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_total$p.adjusted_POST <- p.adjust(corr_spearman_genus_total$p.value_POST, method = "BH", n = 35)


corr_sig_genus_total <- filter(corr_spearman_genus_total, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_total, file = '/Users/student05/Documents/fa feces/tabellen/total.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von total FA und genus-level

```{r}
ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g]') + 
  ylab('log10 (Relative Abundance g__Oscillospira)')+
  facet_wrap(~Time) +
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bifidobacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='total', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Bacteroides)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Sutterella)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='total', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance g__Sutterella')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Ruminococcaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))



ggplot(genus_FA, aes(y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Akkermansia)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__Faecalibacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Rikenellaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='total', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Total fatty acid Concentration [nmol/g]', ylab = 'log10 (Relative Abundance f__Rikenellaceae')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggplot(genus_FA, aes(y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__, x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance f__Barnesiellaceae)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus., x=total)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('total fatty acid Concentration [nmol/g DW]') + 
  ylab('log10 (Relative Abundance g__.Ruminococcus)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop Omega3 FA und genus-level

```{r}

corr_map_genus_Omega3 <- filter(genus_FA, !is.na(Omega3))

corr_spearman_genus_Omega3 <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_Omega3, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Omega3
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Omega3
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Omega3
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_Omega3)+1
  
  corr_spearman_genus_Omega3[nrow,"FA"] = "Omega3"
  
  corr_spearman_genus_Omega3[nrow, "Genus"] = i
  
  corr_spearman_genus_Omega3[nrow, "p.value"] = p
  
  corr_spearman_genus_Omega3[nrow, "rho"] = rho
  
  corr_spearman_genus_Omega3[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_Omega3[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_Omega3[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_Omega3[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_Omega3$p.adjusted <- p.adjust(corr_spearman_genus_Omega3$p.value, method = "BH", n = 35)

corr_spearman_genus_Omega3$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Omega3$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_Omega3$p.adjusted_POST <- p.adjust(corr_spearman_genus_Omega3$p.value_POST, method = "BH", n = 35)


corr_sig_genus_Omega3 <- filter(corr_spearman_genus_Omega3, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_Omega3, file = '/Users/student05/Documents/fa feces/tabellen/Omega3.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


```

Plotten von Omega3 FA und genus-level

```{r}

ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [nmol/g]', cor.coef.coord =c(0, -1.9), ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [nmol/g]', cor.coef.coord =c(0, -1.9), ylab = 'log10 (Relative Abundance g__Oscillospira')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Oscillospira',label = 'Proband')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium., x=Omega3)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance g__Eubacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance f__Coriobacteriaceae')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega3', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))
```

Loop Omega6 FA und genus-level

```{r}
corr_map_genus_Omega6 <- filter(genus_FA, !is.na(Omega6))

corr_spearman_genus_Omega6 <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_Omega6, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Omega6
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Omega6
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Omega6
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_Omega6)+1
  
  corr_spearman_genus_Omega6[nrow,"FA"] = "Omega6"
  
  corr_spearman_genus_Omega6[nrow, "Genus"] = i
  
  corr_spearman_genus_Omega6[nrow, "p.value"] = p
  
  corr_spearman_genus_Omega6[nrow, "rho"] = rho
  
  corr_spearman_genus_Omega6[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_Omega6[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_Omega6[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_Omega6[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_Omega6$p.adjusted <- p.adjust(corr_spearman_genus_Omega6$p.value, method = "BH", n = 35)

corr_spearman_genus_Omega6$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Omega6$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_Omega6$p.adjusted_POST <- p.adjust(corr_spearman_genus_Omega6$p.value_POST, method = "BH", n = 35)


corr_sig_genus_Omega6 <- filter(corr_spearman_genus_Omega6, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_Omega6, file = '/Users/student05/Documents/fa feces/tabellen/Omega6.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

PLotten von Omega6 FA und genus-level

```{r}

ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='Omega6', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance f__Coriobacteriaceae')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Omega6', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop omega6/omega3-ratio und genus-level

```{r}
corr_map_genus_ratio <- filter(genus_FA, !is.na(ratio))

corr_spearman_genus_ratio <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_ratio, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$ratio
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$ratio
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$ratio
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_ratio)+1
  
  corr_spearman_genus_ratio[nrow,"FA"] = "ratio"
  
  corr_spearman_genus_ratio[nrow, "Genus"] = i
  
  corr_spearman_genus_ratio[nrow, "p.value"] = p
  
  corr_spearman_genus_ratio[nrow, "rho"] = rho
  
  corr_spearman_genus_ratio[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_ratio[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_ratio[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_ratio[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_ratio$p.adjusted <- p.adjust(corr_spearman_genus_ratio$p.value, method = "BH", n = 35)

corr_spearman_genus_ratio$p.adjusted_PRE <- p.adjust(corr_spearman_genus_ratio$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_ratio$p.adjusted_POST <- p.adjust(corr_spearman_genus_ratio$p.value_POST, method = "BH", n = 35)


corr_sig_genus_ratio <- filter(corr_spearman_genus_ratio, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_ratio, file = '/Users/student05/Documents/fa feces/tabellen/ratio.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

PLotten von omega6/omega3-ratio und genus-level

```{r}

ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")


ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Lachnospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Lachnospira')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Bacteroides')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")


ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Collinsella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='ratio', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop ungesaettigte FA und genus-level

```{r}

corr_map_genus_unsat <- filter(genus_FA, !is.na(unsat))

corr_spearman_genus_unsat <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_unsat, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$unsat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_unsat)+1
  
  corr_spearman_genus_unsat[nrow,"FA"] = "unsat"
  
  corr_spearman_genus_unsat[nrow, "Genus"] = i
  
  corr_spearman_genus_unsat[nrow, "p.value"] = p
  
  corr_spearman_genus_unsat[nrow, "rho"] = rho
  
  corr_spearman_genus_unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_unsat$p.adjusted <- p.adjust(corr_spearman_genus_unsat$p.value, method = "BH", n = 35)

corr_spearman_genus_unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_unsat$p.value_POST, method = "BH", n = 35)


corr_sig_genus_unsat <- filter(corr_spearman_genus_unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_genus_unsat, file = '/Users/student05/Documents/fa feces/tabellen/unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```


2.5 Omega-Fa Analysen, Aufnahem ueber Nahrung, Ausscheidung, Ausscheidungsrate , Absorptionsrate, EPA, DHA

Laden der Metadaten und testen auf Normalverteilung

```{r}
FA_stool.o <- read.table("/Users/student05/Documents/Omega aufnahme 1.txt", sep = '\t', comment='',head=T)
View(FA_stool)


FA_stool.o <- subset(filter(FA_stool.o, !Proband == "33MP"))


FA_colnames.o <- colnames(FA_stool.o[, c(21:23)])

nd.FA.o <- data.frame()

for (i in FA_colnames.o)  {
  fit <- shapiro.test(as.matrix(as.data.frame(lapply(FA_stool.o[,i],
                                                     as.numeric))))
  p = fit$p.value
  nrow = nrow(nd.FA.o)+1
  nd.FA.o[nrow, "column"] = i
  nd.FA.o[nrow, "p.value"] = round(p, 4)
  
}

```

Plotten der Normalverteilungen

```{r}
ggqqplot(FA_stool.o$Linolensaeure_f, ylab = "Fecal omega 3 FA concentration [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$Linolsaeure_f, ylab = "Fecal omega 6 FA concentration [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$Linolensaeure_i, ylab = "Intake omega 3 FA concentration [g]", xlab = "SampleID")

ggqqplot(FA_stool.o$EPA_i, ylab = "Intake EPA  [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$DHA_i, ylab = "Intake DHA  [g]", xlab = "SampleID")

ggqqplot(FA_stool.o$AR_Linolensaeure, ylab = "precipitation rate omega 3 FA concentration [%]", xlab = "SampleID")
ggqqplot(FA_stool.o$AR_Linolsaeure, ylab = "precipitation rate omega 6 FA concentration [%]", xlab = "SampleID")

ggqqplot(FA_stool.o$A_Linolensaeure, ylab = "Intake in the body omega 3 FA  [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$A_Linolsaeure, ylab = "Intake in the body omega 6 FA [g]", xlab = "SampleID")

ggqqplot(FA_stool.o$AP_Linolensaeure, ylab = "percentage intake in the body omega 3 FA  [g]", xlab = "SampleID")
ggqqplot(FA_stool.o$AP_Linolsaeure, ylab = "percentage intake in the body omega 6 FA [g]", xlab = "SampleID")


```

Filtern nach PRE und POST

```{r}

FA_stool_pairs.o <- filter(FA_stool.o, Proband == "05AP" | Proband == "06WT"
                           
                           | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                           
                           | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                           
                           | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                           
                           | Proband == "31KE" | Proband == "32FG" | Proband == "35AD"| Proband == "36ER"
                           
                           | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                           
                           | Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
                           
                           | Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
                           
                           | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")
FA_stool_pairs.o$Proband



FA_stool_pairs_PP.o <- filter(FA_stool_pairs.o, Time=="PRE" | Time=="POST")

```

Loop fuer Wilcoxon-test zwischen PRE und POST

```{r}
wilcox_FA.o<- data_frame()


for (i in FA_colnames.o) {
  
  tmp <- FA_stool_pairs_PP.o %>% drop_na(i) 
  
  x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
  
  y <- FA_stool_pairs_PP.o$Time 
  
  tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = T)
  
  p <- tmp_wilcox$p.value
  
  nrow = nrow(wilcox_FA.o)+1
  
  wilcox_FA.o[nrow, "FA"] <- i 
  
  
  
  wilcox_FA.o[nrow, "Mean PRE"] <-round(mean(subset(filter(FA_stool_pairs.o,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), 2, mean,  na.rm = TRUE), 4)
  
  wilcox_FA.o[nrow, "sd PRE"] <-round(sd(c(subset(filter(FA_stool_pairs.o,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), na.rm = TRUE)), 4)
  
  wilcox_FA.o[nrow, "Mean POST"] <-round(mean(subset(filter(FA_stool_pairs.o,Time == "POST")[,i],!is.na(i), na.rm = TRUE), 2, mean,  na.rm = TRUE), 4)
  
  wilcox_FA.o[nrow, "sd POST"] <- round(sd(c(subset(filter(FA_stool_pairs.o,Time == "POST")[,i],!is.na(i), na.rm = TRUE),na.rm = TRUE)), 4)
  
  wilcox_FA.o[nrow, "p.value"] <- round(p, 4) }


```

Boxplot der Omega-FA je Zeitpunkt
alle FA
```{r}
FA_stool.melt.o <- melt(FA_stool_pairs.o, id.vars = 'Time', measure.vars = c('Linolensaeure_f', 'Linolsaeure_f', 'Linolensaeure_i', 'Linolsaeure_i'))


FA_stool.melt.o <- rename(FA_stool.melt.o, FA=variable)
FA_stool.melt.o <- rename(FA_stool.melt.o, Concentration=value)


FA_stool.melt.o$Time <- factor(FA_stool.melt.o$Time, levels = c("PRE", "POST"))

ggplot(FA_stool.melt.o,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Time Point') + ylab ('Concentration [g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("omega 3 fecal", "omega 6 fecal","omega 3 intake", "omega 6 intake"), 
                    values = c("tomato", "yellowgreen", "steelblue2", "deeppink2")) +
  theme(legend.position="top")+
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))


```
Omega 3 in mol

```{r}
FA_stool.melt.o1 <- melt(FA_stool_pairs.o, id.vars = c('Time','Proband'), measure.vars = c('Linolensaeure_mol'))


FA_stool.melt.o1 <- rename(FA_stool.melt.o1, FA=variable)
FA_stool.melt.o1 <- rename(FA_stool.melt.o1, Concentration=value)
 
FA_stool.melt.o1$Time <- factor(FA_stool.melt.o1$Time, levels = c("PRE", "POST"))

ggplot(FA_stool.melt.o1,aes(x=Time, y=Concentration, fill= FA),label= 'Proband') +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("omega 3 fecal"), 
                    values = c("tomato")) +
  theme(legend.position="top")+
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
geom_text(aes(label=Proband),hjust=0, vjust=0)


pdf("/Users/student05/Documents/fertige Plots/Linolsäure.probands.pdf",width=7.5, height=10)
ggpaired(FA_stool.melt.o1, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('skyblue','orchid4'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'FA', short.panel.labs = FALSE) +
  xlab('Fäkale alpha-Linolensäurekonzentrationen [nmol/g]') + ylab('Konzentration [nmol/g]')+
  theme(legend.position="top")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))
dev.off()

```

Omega 6 in mol

```{r}
FA_stool.melt.o2 <- melt(FA_stool_pairs.o, id.vars = c('Time','Proband'), measure.vars = c('Linolsaeure_mol'))

 
FA_stool.melt.o2 <- rename(FA_stool.melt.o2, FA=variable)
FA_stool.melt.o2<- rename(FA_stool.melt.o2, Concentration=value)

FA_stool.melt.o2$Time <- factor(FA_stool.melt.o2$Time, levels = c("PRE", "POST"))

ggplot(FA_stool.melt.o2,aes(x=Time, y=Concentration, fill= FA),label= 'Proband') +
  xlab ('Time Point') + ylab ('Concentration [nmol/g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("omega 6 fecal"), 
                    values = c("yellowgreen")) +
  theme(legend.position="top")+
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  geom_text(aes(label=Proband),hjust=0, vjust=0)


ggpaired(FA_stool.melt.o2, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'FA', short.panel.labs = FALSE) +
  xlab('fecal omega 6') + ylab('Concentration [nmol/g DW]') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)

```

Omega 3 Aufnahme PRE und POST

```{r}
FA_stool.melt.o3 <- melt(FA_stool_pairs.o, id.vars = c('Time','Proband'), measure.vars = c('Linolensaeure_i'))


FA_stool.melt.o3 <- rename(FA_stool.melt.o3, FA=variable)
FA_stool.melt.o3 <- rename(FA_stool.melt.o3, Concentration=value)

 
FA_stool.melt.o3$Time <- factor(FA_stool.melt.o3$Time, levels = c("PRE", "POST"))

ggplot(FA_stool.melt.o3,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Time Point') + ylab ('Concentration [g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("omega 3 intake", "omega 6 intake"), 
                    values = c("steelblue2")) +
  theme(legend.position="top")+
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  geom_text(aes(label=Proband),hjust=0, vjust=0)


ggpaired(FA_stool.melt.o3, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'FA', short.panel.labs = FALSE) +
  xlab('intake omega 3') + ylab('Concentration [nmol/g DW]') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)

```

Omega 6 Aufnahme PRE und POST

```{r}
FA_stool.melt.o4 <- melt(FA_stool_pairs.o, id.vars = c('Time','Proband'), measure.vars = c('Linolsaeure_i'))

 
FA_stool.melt.o4 <- rename(FA_stool.melt.o4, FA=variable)
FA_stool.melt.o4 <- rename(FA_stool.melt.o4, Concentration=value)


FA_stool.melt.o4$Time <- factor(FA_stool.melt.o4$Time, levels = c("PRE", "POST"))

ggplot(FA_stool.melt.o4,aes(x=Time, y=Concentration, fill= FA)) +
  xlab ('Time Point') + ylab ('Concentration [g]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("omega 6 intake"), 
                    values = c("deeppink")) +
  theme(legend.position="top")+
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  geom_text(aes(label=Proband),hjust=0, vjust=0)


ggpaired(FA_stool.melt.o4, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'FA', short.panel.labs = FALSE) +
  xlab('intake omega 6') + ylab('Concentration [nmol/g DW]') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)
```
```{r}

```

3.3 Korrelationsanalysen Omega-FA und Taxa
Metadaten hochladen, filtern und synchronisieren

```{r}

relab_means <- read.table('/Users/student05/Documents/relative abundance/relab_means_per_timepoint.txt', sep ='\t', comment='', head=T)

relab_means_melt <- melt(relab_means, id=c('Proband', 'Time'))

relab_means_melt <- dplyr::rename(relab_means_melt, Taxa=variable)

relab_means_melt <- dplyr::rename(relab_means_melt, Relative_Abundance=value)

relab_phylum <- subset(relab_means_melt, !grepl("g__|f__|o__|c__", relab_means_melt$Taxa))

relab_phylum <- subset(relab_phylum, !grepl("k__Archaea", relab_phylum$Taxa))

relab_phylum$Time <- factor(relab_phylum$Time, levels=c('PRE','POST','FOLLOW-UP'))

relab_phylum_spread <- spread(relab_phylum, Taxa, Relative_Abundance, sep = NULL)

relab_genus <- subset(relab_means_melt, grepl("g__", relab_means_melt$Taxa))

relab_genus <- subset(relab_genus, !grepl("k__Archaea", relab_genus$Taxa))

relab_genus$Time <- factor(relab_genus$Time, levels = c('PRE','POST','FOLLOW-UP'))

relab_genus_spread <- spread(relab_genus, Taxa, Relative_Abundance, sep = NULL)


FA_stool.o <- read.table("/Users/student05/Documents/fa feces/FA fecal/omega/Omega aufnahme .txt", sep = '\t', comment='',head=T)
View(FA_stool)


FA_stool.o <- subset(filter(FA_stool.o, !Proband == "33MP"))


relab_phylum_ID <- relab_phylum_spread

relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))

row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID

relab_genus_ID <- relab_genus_spread

relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))

row.names(relab_genus_ID) <- relab_genus_ID$SampleID

FA_stool.o$Proband

FA_stool.o <- subset(filter(FA_stool.o, !Proband == "34WF",!Proband == "49RJ"))

FA_stool.o <- mutate(FA_stool.o, SampleID1 = paste(Proband, Time, sep = "."))

row.names(FA_stool.o) <- FA_stool.o$SampleID1

common.ids.relab <- intersect(rownames(FA_stool.o), rownames(relab_phylum_ID))

FA_stool.o <- FA_stool.o[common.ids.relab,]

relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]

```
Phylum-Level log transformation hinzufuegen von Pseudocount von 0.00001

```{r}
relab_phylum_ID_log <- relab_phylum_ID[,c(3:8)] + 0.00001

relab_phylum_ID_log <- log10(relab_phylum_ID_log)

phylum_FA <- cbind(relab_phylum_ID_log, FA_stool.o[, c(2:23)])
```

Loop Korrelation faekale Linolensaeure und phylum-level

```{r}
corr_map_phylum_omega6f <- filter(phylum_FA, !is.na(Linolsaeure_f))

corr_spearman_Phylum_omega6f <- data.frame()

for( i in phylum_colnames) {
  
  
  tmp <- filter(corr_map_phylum_omega6f, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Linolsaeure_f

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Linolsaeure_f
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Linolsaeure_f
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_omega6f)+1
  
  corr_spearman_Phylum_omega6f[nrow,"FA"] <- "Linoleic fa"
  
  corr_spearman_Phylum_omega6f[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_omega6f[nrow, "p.value"] = p
  
  corr_spearman_Phylum_omega6f[nrow, "rho"] = rho
  
  corr_spearman_Phylum_omega6f[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_omega6f[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_omega6f[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_omega6f[nrow, "rho_POST"] = rho_POST
  
}



corr_spearman_Phylum_omega6f$p.adjusted <- p.adjust(corr_spearman_Phylum_omega6f$p.value, method = "BH", n = 35)

corr_spearman_Phylum_omega6f$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega6f$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_omega6f$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega6f$p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_omega6f <- filter(corr_spearman_Phylum_omega6f, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

```

Plotten von faekaler Omega6 FA und Phylum-level Korrelationen


```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=Linolsaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='Linolsaeure_f', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label = 'Proband')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='Linolsaeure_f', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggscatter(phylum_FA, x='Linolensaeure_f', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord =c(0, -0.8), xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='Linolensaeure_f', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord =c(0, -0.8), xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
 
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(phylum_FA, x='Linolsaeure_f', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='Linolensaeure_f', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=Linolsaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Linolsaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=Linolsaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=Linolsaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


```

Loop Linolensaeure und phylum-level

```{r}
corr_map_phylum_omega3f <- filter(phylum_FA, !is.na(Linolsaeure_f))

corr_spearman_Phylum_omega3f <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_omega3f, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$Linolensaeure_f

  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Linolensaeure_f
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Linolensaeure_f
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_omega6f)+1
  
  corr_spearman_Phylum_omega3f[nrow,"FA"] <- "Linolenic fa"
  
  corr_spearman_Phylum_omega3f[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_omega3f[nrow, "p.value"] = p
  
  corr_spearman_Phylum_omega3f[nrow, "rho"] = rho
  
  corr_spearman_Phylum_omega3f[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_omega3f[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_omega3f[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_omega3f[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_omega3f$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3f$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_omega3f$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3f$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_omega3f$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3f$p.value_POST, method = "BH", n = 35)

```

Plotten von Korrelationen zwischen Omega3-FA und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=Linolensaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='Linolensaeure_f', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label='Proband')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=Linolensaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Linolensaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")



```

Loop Omega6 Aufnahme und phylum-level

```{r}
corr_map_phylum_omega6i <- filter(phylum_FA, !is.na(Linolsaeure_i))

corr_spearman_Phylum_omega6i <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_omega6i, !is.na(i))

  y = tmp[,i]
  
  x = tmp$Linolsaeure_i
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Linolsaeure_i
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Linolsaeure_i
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_omega6i)+1
 
  corr_spearman_Phylum_omega6i[nrow,"FA"] <- "Linoleic fa i"
  
  corr_spearman_Phylum_omega6i[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_omega6i[nrow, "p.value"] = p
  
  corr_spearman_Phylum_omega6i[nrow, "rho"] = rho
  
  corr_spearman_Phylum_omega6i[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_omega6i[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_omega6i[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_omega6i[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_omega6i$p.adjusted <- p.adjust(corr_spearman_Phylum_omega6i$p.value, method = "BH", n = 35)

corr_spearman_Phylum_omega6i$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega6i$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_omega6i$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega6i$p.value_POST, method = "BH", n = 35)

```

Plotten von Korrelationen zwischen Omega6 Aufnahme und phylum-level

```{r}
phylum_FA$Time <- factor(phylum_FA$Time, levels = c("PRE", "POST"))

ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Linolsaeure_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='Linolsaeure_i', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration intake [g]', ylab = 'log10 (Relative Abundance p__Actinobacteria)', label = 'Proband')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=Linolsaeure_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=Linolsaeure_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

```

Loop Omega3 Aufnahme und phylum-level

```{r}
corr_map_phylum_omega3i <- filter(phylum_FA, !is.na(Linolensaeure_i))

corr_spearman_Phylum_omega3i <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_omega3i, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$Linolensaeure_i

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Linolensaeure_i
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Linolensaeure_i
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_omega3i)+1
 
  corr_spearman_Phylum_omega3i[nrow,"FA"] <- "Linolenic fa i"
  
  corr_spearman_Phylum_omega3i[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_omega3i[nrow, "p.value"] = p
  
  corr_spearman_Phylum_omega3i[nrow, "rho"] = rho
  
  corr_spearman_Phylum_omega3i[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_omega3i[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_omega3i[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_omega3i[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_omega3i$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3i$p.value, method = "BH", n = 35)

corr_spearman_Phylum_omega3i$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3i$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_omega3i$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3i$p.value_POST, method = "BH", n = 35)

```

Plotten Omega3 Aufnahme und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=Linolensaeure_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='Linolensaeure_i', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration intake [g]', ylab = 'log10 (Relative Abundance p__Actinobacteria)',label = 'Proband')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=Linolensaeure_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=Linolensaeure_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


```

Loop Omega3 FA Absorption in g

```{r}
corr_map_phylum_omega3a <- filter(phylum_FA, !is.na(A_Linolensaeure))

corr_spearman_Phylum_omega3a <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_omega3a, !is.na(i))

  y = tmp[,i]
  
  x = tmp$A_Linolensaeure
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$A_Linolensaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$A_Linolensaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_omega3a)+1
 
  corr_spearman_Phylum_omega3a[nrow,"FA"] <- "Linolenic fa body i"
  
  corr_spearman_Phylum_omega3a[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_omega3a[nrow, "p.value"] = p
  
  corr_spearman_Phylum_omega3a[nrow, "rho"] = rho
  
  corr_spearman_Phylum_omega3a[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_omega3a[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_omega3a[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_omega3a[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_omega3a$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3a$p.value, method = "BH", n = 35)

corr_spearman_Phylum_omega3a$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3a$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_omega3a$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3a$p.value_POST, method = "BH", n = 35)

```

Plotten von Omega3 Aufnahme in g und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=A_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=A_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=A_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration intake [g]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")
```

Loop Omega3 Ausscheidungsrate in Prozent und phylum-level

```{r}
corr_map_phylum_omega3ar <- filter(phylum_FA, !is.na(AR_Linolensaeure))

corr_spearman_Phylum_omega3ar <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_omega3ar, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$AR_Linolensaeure

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$AR_Linolensaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$AR_Linolensaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_omega3ar)+1
  
  corr_spearman_Phylum_omega3ar[nrow,"FA"] <- "Linolenic fa Precipitation"
  
  corr_spearman_Phylum_omega3ar[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_omega3ar[nrow, "p.value"] = p
  
  corr_spearman_Phylum_omega3ar[nrow, "rho"] = rho
  
  corr_spearman_Phylum_omega3ar[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_omega3ar[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_omega3ar[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_omega3ar[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_omega3ar$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3ar$p.value, method = "BH", n = 35)

corr_spearman_Phylum_omega3ar$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3ar$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_omega3ar$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3ar$p.value_POST, method = "BH", n = 35)


```

Plotten Omega3 Ausscheidungsrate in Prozent und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=AR_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=AR_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=AR_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=AR_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=AR_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop Omega6 Ausscheidungsrate in Prozent und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=AR_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=AR_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label= 'Proband')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=AR_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=AR_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=AR_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid excretion rate [%]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label = 'Proband')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=AR_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


```

Loop Omega6 Absorption in Prozent und phylum-level

```{r}
corr_map_phylum_omega6ap <- filter(phylum_FA, !is.na(AP_Linolsaeure))

corr_spearman_Phylum_omega6ap <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_omega6ap, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$AP_Linolsaeure
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$AP_Linolsaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$AP_Linolsaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_omega6ap)+1
 
  corr_spearman_Phylum_omega6ap[nrow,"FA"] <- "Linoleic fa i [%]"
  
  corr_spearman_Phylum_omega6ap[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_omega6ap[nrow, "p.value"] = p
  
  corr_spearman_Phylum_omega6ap[nrow, "rho"] = rho
  
  corr_spearman_Phylum_omega6ap[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_omega6ap[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_omega6ap[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_omega6ap[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_omega6ap$p.adjusted <- p.adjust(corr_spearman_Phylum_omega6ap$p.value, method = "BH", n = 35)

corr_spearman_Phylum_omega6ap$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega6ap$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_omega6ap$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega6ap$p.value_POST, method = "BH", n = 35)

```

Plotten von Omega6 Absorption in Prozent und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=AP_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=AP_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=AP_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=AP_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=AP_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=AR_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

```

Loop Omega3 Absorption in Prozent und phylum-level

```{r}
corr_map_phylum_omega3ap <- filter(phylum_FA, !is.na(AP_Linolensaeure))

corr_spearman_Phylum_omega3ap <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_omega3ap, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$AP_Linolensaeure
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$AP_Linolensaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$AP_Linolensaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_omega3ap)+1
  
  corr_spearman_Phylum_omega3ap[nrow,"FA"] <- "Linolenic fa i [%]"
  
  corr_spearman_Phylum_omega3ap[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_omega3ap[nrow, "p.value"] = p
  
  corr_spearman_Phylum_omega3ap[nrow, "rho"] = rho
  
  corr_spearman_Phylum_omega3ap[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_omega3ap[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_omega3ap[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_omega3ap[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_omega3ap$p.adjusted <- p.adjust(corr_spearman_Phylum_omega3ap$p.value, method = "BH", n = 35)

corr_spearman_Phylum_omega3ap$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_omega3ap$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_omega3ap$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_omega3ap$p.value_POST, method = "BH", n = 35)

```

Plotten von Omega3 Absorption in Prozent und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=AP_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=AP_Linolensaeure)) + geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid intake rate [%]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=AP_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=AP_Linolensaeure)) + geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid intake rate [%]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Bacteroidetes, x=AP_Linolensaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid intake rate [%]') + 
  ylab('log10 (Relative Abundance p__Bacteroidetes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=AR_Linolsaeure)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linoleic fatty acid Precipitation rate [%]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

```

Loop EPA und phylum-level

```{r}

corr_map_phylum_epa <- filter(phylum_FA, !is.na(EPA_i))

corr_spearman_Phylum_epa <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_epa, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$EPA_i
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$EPA_i
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$EPA_i
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_epa)+1

  corr_spearman_Phylum_epa[nrow,"FA"] <- "EPA intake [g]"
  
  corr_spearman_Phylum_epa[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_epa[nrow, "p.value"] = p
  
  corr_spearman_Phylum_epa[nrow, "rho"] = rho
  
  corr_spearman_Phylum_epa[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_epa[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_epa[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_epa[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_epa$p.adjusted <- p.adjust(corr_spearman_Phylum_epa$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_epa$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_epa$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_epa$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_epa$p.value_POST, method = "BH", n = 35)

```

Plotten von EPA und phylum-level 

```{r}
ggscatter(phylum_FA, x='EPA_i', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label= 'Proband')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=EPA_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('EPA intake [g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


ggplot(phylum_FA, aes(y=k__Bacteria.p__Verrucomicrobia, x=EPA_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('EPA intake [g]') + 
  ylab('log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=EPA_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('EPA intake [g]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")
```

Loop DHA und phylum-level

```{r}
corr_map_phylum_dha <- filter(phylum_FA, !is.na(DHA_i))

corr_spearman_Phylum_dha <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_dha, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$DHA_i
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$DHA_i
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$DHA_i
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_dha)+1
  
  corr_spearman_Phylum_dha[nrow,"FA"] <- "DHA intake [g]"
  
  corr_spearman_Phylum_dha[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_dha[nrow, "p.value"] = p
  
  corr_spearman_Phylum_dha[nrow, "rho"] = rho
  
  corr_spearman_Phylum_dha[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_dha[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_dha[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_dha[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_dha$p.adjusted <- p.adjust(corr_spearman_Phylum_dha$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_dha$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_dha$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_dha$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_dha$p.value_POST, method = "BH", n = 35)

```

Plotten von DHA und phylum-level Korrelationen

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=DHA_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('DHA intake [g]') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Tenericutes, x=DHA_i)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('DHA intake [g]') + 
  ylab('log10 (Relative Abundance p__Tenericutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggscatter(phylum_FA, x='DHA_i', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)',label= 'Proband')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")
```

Loop faekales Omega6/Omega3-ratio und phylum-level

```{r}

corr_map_phylum_ra <- filter(phylum_FA, !is.na(ratio_f))

corr_spearman_Phylum_ra <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_ra, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$ratio_f
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$ratio_f
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$ratio_f
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_ra)+1
 
  corr_spearman_Phylum_ra[nrow,"FA"] <- "ratio omega6/omega3 fecal"
  
  corr_spearman_Phylum_ra[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_ra[nrow, "p.value"] = p
  
  corr_spearman_Phylum_ra[nrow, "rho"] = rho
  
  corr_spearman_Phylum_ra[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_ra[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_ra[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_ra[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_ra$p.adjusted <- p.adjust(corr_spearman_Phylum_ra$p.value, method = "BH", n = 35)

corr_spearman_Phylum_ra$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_ra$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_ra$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_ra$p.value_POST, method = "BH", n = 35)

```

Plotten von faekalem Omega6/Omega3-ratio und phylum-level

```{r}
ggplot(phylum_FA, aes(y=k__Bacteria.p__Actinobacteria, x=ratio_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') + 
  ylab('log10 (Relative Abundance p__Actinobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Proteobacteria, x=ratio_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') + 
  ylab('log10 (Relative Abundance p__Proteobacteria)')+
  facet_wrap(~Time)+
  theme(legend.position="top")

ggplot(phylum_FA, aes(y=k__Bacteria.p__Firmicutes, x=ratio_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('omega 6/omega 3 ratio fecal') + 
  ylab('log10 (Relative Abundance p__Firmicutes)')+
  facet_wrap(~Time)+
  theme(legend.position="top")


```

Genus-level, Filtern der Metadaten, log transformation und hinzufuegen von Pseudocount 0.00001


```{r}
genus_colnames <- colnames(relab_genus_spread[, c(3:31)])

relab_genus_ID_log <- relab_genus_ID[,c(3:31)] + 0.00001

relab_genus_ID_log <- log10(relab_genus_ID_log)

genus_FA <- cbind(relab_genus_ID_log, FA_stool.o)
genus_FA$Time <- factor(genus_FA$Time, levels = c("PRE", "POST"))


```


Loop faekale Omega3 FA und genus-level

```{r}
corr_map_genus_omega3f <- filter(genus_FA, !is.na(Linolensaeure_f))

corr_spearman_genus_omega3f <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omega3f, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Linolensaeure_f
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Linolensaeure_f
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Linolensaeure_f
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omega3f)+1
  
  corr_spearman_genus_omega3f[nrow,"FA"] = "Linolenic fa fecal"
  
  corr_spearman_genus_omega3f[nrow, "Genus"] = i
  
  corr_spearman_genus_omega3f[nrow, "p.value"] = p
  
  corr_spearman_genus_omega3f[nrow, "rho"] = rho
  
  corr_spearman_genus_omega3f[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omega3f[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omega3f[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omega3f[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omega3f$p.adjusted <- p.adjust(corr_spearman_genus_omega3f$p.value, method = "BH", n = 35)

corr_spearman_genus_omega3f$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega3f$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_omega3f$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3f$p.value_POST, method = "BH", n = 35)

```

Plotten faekale Omega3 FA und genus-level

```{r}
ggscatter(genus_FA, x='Linolensaeure_mol', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [nmol/g]', cor.coef.coord =c(0, -1.9), ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(genus_FA, x='Linolensaeure_mol', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [nmol/g]', cor.coef.coord =c(0, -1.9), ylab = 'log10 (Relative Abundance g__Oscillospira')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Oscillospira',label = 'Proband')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggplot(genus_FA, aes(y=k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium., x=Linolensaeure_f)) + 
  geom_point(aes(color=Time)) + scale_color_manual(values = c('yellowgreen', 'coral2', 'steelblue2')) + 
  geom_smooth(method = 'lm', color='grey65') + xlab('Linolenic fatty acid Concentration fecal [g]') + 
  ylab('log10 (Relative Abundance g__Eubacterium)')+
  facet_wrap(~Time)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))



ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance f__Coriobacteriaceae')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolensaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))
```


Loop faekale Omega6-FA und genus-level

```{r}
corr_map_genus_omega6f <- filter(genus_FA, !is.na(Linolsaeure_f))

corr_spearman_genus_omega6f <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omega6f, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Linolsaeure_f
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Linolensaeure_f
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Linolsaeure_f
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omega6f)+1
  
  corr_spearman_genus_omega6f[nrow,"FA"] = "Linoleic fa fecal"
  
  corr_spearman_genus_omega6f[nrow, "Genus"] = i
  
  corr_spearman_genus_omega6f[nrow, "p.value"] = p
  
  corr_spearman_genus_omega6f[nrow, "rho"] = rho
  
  corr_spearman_genus_omega6f[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omega6f[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omega6f[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omega6f[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omega6f$p.adjusted <- p.adjust(corr_spearman_genus_omega6f$p.value, method = "BH", n = 35)

corr_spearman_genus_omega6f$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega6f$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_omega6f$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega6f$p.value_POST, method = "BH", n = 35)

```

Plotten faekale Omega6-FA und genus-level

```{r}

ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='Linolsaeure_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Concentration fecal [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop prozentuale Ausscheidungsrate Omega3-FA und genus-level

```{r}
corr_map_genus_omega3ar <- filter(genus_FA, !is.na(AR_Linolensaeure))

corr_spearman_genus_omega3ar <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omega3ar, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$AR_Linolensaeure
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$AR_Linolensaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$AR_Linolensaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omega3ar)+1
  
  corr_spearman_genus_omega3ar[nrow,"FA"] = "Linolenic fa Precipitation rate "
  
  corr_spearman_genus_omega3ar[nrow, "Genus"] = i
  
  corr_spearman_genus_omega3ar[nrow, "p.value"] = p
  
  corr_spearman_genus_omega3ar[nrow, "rho"] = rho
  
  corr_spearman_genus_omega3ar[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omega3ar[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omega3ar[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omega3ar[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omega3ar$p.adjusted <- p.adjust(corr_spearman_genus_omega3ar$p.value, method = "BH", n = 35)

corr_spearman_genus_omega3ar$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega3ar$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_omega3ar$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3ar$p.value_POST, method = "BH", n = 35)

```


Plotten prozentuale Ausscheidungsrate Omega3-FA und genus-level

```{r}
ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Blautia')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop prozentuale Ausscheidungsrate Omega6-FA und genus-level

```{r}
corr_map_genus_omega6ar <- filter(genus_FA, !is.na(AR_Linolsaeure))

corr_spearman_genus_omega6ar <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omega6ar, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$AR_Linolsaeure
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$AR_Linolsaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$AR_Linolsaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omega6ar)+1
  
  corr_spearman_genus_omega6ar[nrow,"FA"] = "Linoleic fa Precipitation rate "
  
  corr_spearman_genus_omega6ar[nrow, "Genus"] = i
  
  corr_spearman_genus_omega6ar[nrow, "p.value"] = p
  
  corr_spearman_genus_omega6ar[nrow, "rho"] = rho
  
  corr_spearman_genus_omega6ar[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omega6ar[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omega6ar[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omega6ar[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omega6ar$p.adjusted <- p.adjust(corr_spearman_genus_omega6ar$p.value, method = "BH", n = 35)

corr_spearman_genus_omega6ar$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega6ar$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_omega6ar$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega6ar$p.value_POST, method = "BH", n = 35)

```


Plotten prozentuale Omega6 Ausscheidungsrate und genus-level

```{r}

ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Prevotella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Blautia')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AR_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance f__Rikenellaceae')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AR_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Dorea')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop absorbierte Omega3-FA in g und genus-level

```{r}
corr_map_genus_omega3a <- filter(genus_FA, !is.na(A_Linolensaeure))

corr_spearman_genus_omega3a <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omega3a, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$A_Linolensaeure
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$A_Linolensaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$A_Linolensaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omega3a)+1
  
  corr_spearman_genus_omega3a[nrow,"FA"] = "Linolenic fa intake into the body "
  
  corr_spearman_genus_omega3a[nrow, "Genus"] = i
  
  corr_spearman_genus_omega3a[nrow, "p.value"] = p
  
  corr_spearman_genus_omega3a[nrow, "rho"] = rho
  
  corr_spearman_genus_omega3a[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omega3a[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omega3a[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omega3a[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omega3a$p.adjusted <- p.adjust(corr_spearman_genus_omega3a$p.value, method = "BH", n = 35)

corr_spearman_genus_omega3a$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega3a$p.value_PRE, method = "BH", n = 35)


corr_spearman_genus_omega3a$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3a$p.value_POST, method = "BH", n = 35)

```

Plotten absorbierte Omega3-FA in g und genus-level

```{r}

ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid Precipitation rate [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Prevotella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__.Barnesiellaceae..g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance f__Barnesiellaceae')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Lachnospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Lachnospira')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='A_Linolensaeure', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Collinsella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop absorbierte Omega6-FA in g und genus-level

```{r}
corr_map_genus_omega6a <- filter(genus_FA, !is.na(A_Linolsaeure))

corr_spearman_genus_omega6a <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omega6a, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$A_Linolsaeure
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$A_Linolsaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$A_Linolsaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omega6a)+1
  
  corr_spearman_genus_omega6a[nrow,"FA"] = "Linoleic fa intake into the body "
  
  corr_spearman_genus_omega6a[nrow, "Genus"] = i
  
  corr_spearman_genus_omega6a[nrow, "p.value"] = p
  
  corr_spearman_genus_omega6a[nrow, "rho"] = rho
  
  corr_spearman_genus_omega6a[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omega6a[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omega6a[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omega6a[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omega6a$p.adjusted <- p.adjust(corr_spearman_genus_omega6a$p.value, method = "BH", n = 35)

corr_spearman_genus_omega6a$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega6a$p.value_PRE, method = "BH", n = 35)


corr_spearman_genus_omega6a$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega6a$p.value_POST, method = "BH", n = 35)

```

Plotten absorbierte Omega6-FA in g und genus-level

```{r}
ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absiorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absiorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absiorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Dorea')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Prevotella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(genus_FA, x='A_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [g]', ylab = 'log10 (Relative Abundance g__Dialster')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop prozentuale Omega3-FA Absorption und genus-level

```{r}

corr_map_genus_omega3ap <- filter(genus_FA, !is.na(AP_Linolensaeure))

corr_spearman_genus_omega3ap <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omega3ap, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$AP_Linolensaeure
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$AP_Linolensaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$AP_Linolensaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omega3ap)+1
  
  corr_spearman_genus_omega3ap[nrow,"FA"] = "Linolenic fa intake into the body [%] "
  
  corr_spearman_genus_omega3ap[nrow, "Genus"] = i
  
  corr_spearman_genus_omega3ap[nrow, "p.value"] = p
  
  corr_spearman_genus_omega3ap[nrow, "rho"] = rho
  
  corr_spearman_genus_omega3ap[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omega3ap[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omega3ap[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omega3ap[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omega3ap$p.adjusted <- p.adjust(corr_spearman_genus_omega3ap$p.value, method = "BH", n = 35)

corr_spearman_genus_omega3ap$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega3ap$p.value_PRE, method = "BH", n = 35)


corr_spearman_genus_omega3ap$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3ap$p.value_POST, method = "BH", n = 35)

```

Plotten prozentuale Omega3-FA Absorption und genus-level

```{r}
ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance f__Lachnospiraceae')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))



ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))



ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AP_Linolensaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linolenic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

```

Loop prozentuale Omega6-FA Absorption und genus-level

```{r}
corr_map_genus_omega6ap <- filter(genus_FA, !is.na(AP_Linolsaeure))

corr_spearman_genus_omega6ap <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omega6ap, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$AP_Linolsaeure
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$AP_Linolsaeure
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$AP_Linolsaeure
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omega6ap)+1
  
  corr_spearman_genus_omega6ap[nrow,"FA"] = "Linoleic fa intake into the body [%] "
  
  corr_spearman_genus_omega6ap[nrow, "Genus"] = i
  
  corr_spearman_genus_omega6ap[nrow, "p.value"] = p
  
  corr_spearman_genus_omega6ap[nrow, "rho"] = rho
  
  corr_spearman_genus_omega6ap[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omega6ap[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omega6ap[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omega6ap[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omega6ap$p.adjusted <- p.adjust(corr_spearman_genus_omega6ap$p.value, method = "BH", n = 35)

corr_spearman_genus_omega6ap$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omega6ap$p.value_PRE, method = "BH", n = 35)


corr_spearman_genus_omega3ap$p.adjusted_POST <- p.adjust(corr_spearman_genus_omega3ap$p.value_POST, method = "BH", n = 35)

```


Plotten prozentuale Omega6-FA Absorption und genus-level

```{r}
ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Streptococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))



ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Dorea')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='AP_Linolsaeure', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Linoleic fatty acid absorbed into the body [%]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop EPA-Aufnahme und genus-level

```{r}
corr_map_genus_omegaepa <- filter(genus_FA, !is.na(EPA_i))

corr_spearman_genus_omegaepa <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omegaepa, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$EPA_i
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$EPA_i
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$EPA_i
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omegaepa)+1
  
  corr_spearman_genus_omegaepa[nrow,"FA"] = "EPA intake [g] "
  
  corr_spearman_genus_omegaepa[nrow, "Genus"] = i
  
  corr_spearman_genus_omegaepa[nrow, "p.value"] = p
  
  corr_spearman_genus_omegaepa[nrow, "rho"] = rho
  
  corr_spearman_genus_omegaepa[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omegaepa[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omegaepa[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omegaepa[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omegaepa$p.adjusted <- p.adjust(corr_spearman_genus_omegaepa$p.value, method = "BH", n = 35)

corr_spearman_genus_omegaepa$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omegaepa$p.value_PRE, method = "BH", n = 35)


corr_spearman_genus_omegaepa$p.adjusted_POST <- p.adjust(corr_spearman_genus_omegaepa$p.value_POST, method = "BH", n = 35)

```

Plotten von EPA-Aufnahme und genus-level

```{r}

ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))






ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Akkermansia')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='EPA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'EPA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Dialster')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```


Loop DHA-Aufnahme und genus-level

```{r}
corr_map_genus_omegadha <- filter(genus_FA, !is.na(DHA_i))

corr_spearman_genus_omegadha <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_omegadha, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$DHA_i
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$DHA_i
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$DHA_i
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_omegadha)+1
  
  corr_spearman_genus_omegadha[nrow,"FA"] = "DHA intake [g] "
  
  corr_spearman_genus_omegadha[nrow, "Genus"] = i
  
  corr_spearman_genus_omegadha[nrow, "p.value"] = p
  
  corr_spearman_genus_omegadha[nrow, "rho"] = rho
  
  corr_spearman_genus_omegadha[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_omegadha[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_omegadha[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_omegadha[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_omegadha$p.adjusted <- p.adjust(corr_spearman_genus_omegadha$p.value, method = "BH", n = 35)

corr_spearman_genus_omegadha$p.adjusted_PRE <- p.adjust(corr_spearman_genus_omegadha$p.value_PRE, method = "BH", n = 35)


corr_spearman_genus_omegadha$p.adjusted_POST <- p.adjust(corr_spearman_genus_omegadha$p.value_POST, method = "BH", n = 35)

```

Plotten DHA-Aufnahme und genus-level

```{r}
ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Eubacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Blautia')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Bacteroides')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='DHA_i', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'DHA fatty acid intake [g]', ylab = 'log10 (Relative Abundance g__Dialster')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

Loop fäkales Omega6/Omega3-ratio und genus-level

```{r}
corr_map_genus_ra <- filter(genus_FA, !is.na(ratio_f))

corr_spearman_genus_ra <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_ra, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$ratio_f
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$ratio_f
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$ratio_f
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_ra)+1
  
  corr_spearman_genus_ra[nrow,"FA"] = "omega6/omega3 ratio "
  
  corr_spearman_genus_ra[nrow, "Genus"] = i
  
  corr_spearman_genus_ra[nrow, "p.value"] = p
  
  corr_spearman_genus_ra[nrow, "rho"] = rho
  
  corr_spearman_genus_ra[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_ra[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_ra[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_ra[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_ra$p.adjusted <- p.adjust(corr_spearman_genus_ra$p.value, method = "BH", n = 35)

corr_spearman_genus_ra$p.adjusted_PRE <- p.adjust(corr_spearman_genus_ra$p.value_PRE, method = "BH", n = 35)


corr_spearman_genus_ra$p.adjusted_POST <- p.adjust(corr_spearman_genus_ra$p.value_POST, method = "BH", n = 35)

```

Plotten Omega6/Omega3-ratio und genus-level

```{r}

ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Oscillospira')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")



ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Lachnospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Lachnospira')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))



ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Bacteroides')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")



ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Collinsella')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))



ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Ruminococcus')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Phascolarctobacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggscatter(genus_FA, x='ratio_f', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'omega 6/omega 3 ratio fecal', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black", angle = 90))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


```

4. Sättigungstypen
Einteilung der Sättigungstypen nach fäkaler Fettsäuresättigung: sat, unsat, change.sat, change.unsat
Laden der Metadaten
Vergleich der Aufnahme von Fettsäuren über Nahrung zwischen den Sättigungstypen

```{r}
FA_stool.ST <- read.table("/Users/student05/Documents/fa saturation mit intake types.txt", sep = '\t', comment='',
                          head=T)

View(FA_stool.ST)

FA_stool.ST$Time <-factor(FA_stool.ST$Time, levels = c("PRE", "POST"))

row.names(FA_stool.ST) <- FA_stool.ST$SampleID

FA_stool.ST$Proband

FA_stool.ST <- subset(filter(FA_stool.ST, !SampleID == "ST.35AD.0U1"))

FA_stool.ST <- subset(filter(FA_stool.ST, !Proband == "33MP", !Proband == "35AD", !Proband == "34WF", !Proband == "49RJ"))

comparison_sat <- list(c("sat", "unsat"))

comparison_change <- list(c("change.sat", "change.unsat"))

comparison_time <- list(c("PRE", "POST"))
```

Korrelationen durch die Nahrung aufgenommene ungesättigte FA mit fäkalen ungesättigten FA

```{r}
stool.melt.unsat <- melt(FA_stool.ST, id.vars = c('Time','Proband'), measure.vars = c('unsat', 'unsat.i'))

stool.melt.unsat<- dplyr::rename(stool.melt.unsat, FA=variable)
stool.melt.unsat<- dplyr::rename(stool.melt.unsat, Concentration=value)

stool.melt.unsat$Time <- factor(stool.melt.unsat$Time, levels = c("PRE", "POST"))


ggpaired(stool.melt.unsat, x='FA', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('unsaturated fatty acids fecal and intake') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")+
  theme(legend.position="none")

ggscatter(FA_stool.ST, x='unsat', y='unsat.i',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'Unsaturated fatty acids concentrations intake [g]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='unsat', y='unsat.i', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'Unsaturated fatty acids concentrations intake [g]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Korrelationen durch die Nahrung aufgenommene gesättigte FA mit fäkalen gesättigten FA

```{r}
stool.melt.sat <- melt(FA_stool.ST, id.vars = c('Time','Proband'), measure.vars = c('sat', 'sat.i'))

stool.melt.sat<- dplyr::rename(stool.melt.sat, FA=variable)
stool.melt.sat<- dplyr::rename(stool.melt.sat, Concentration=value)

stool.melt.sat$Time <- factor(stool.melt.sat$Time, levels = c("PRE", "POST"))


ggpaired(stool.melt.sat, x='FA', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('saturated fatty acids fecal and intake') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")+
  theme(legend.position="none")

ggscatter(FA_stool.ST, x='sat', y='sat.i',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'Saturated fatty acids concentrations intake [g]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='sat', y='sat.i', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'Saturated fatty acids concentrations intake [g]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Wilcoxon-Test zwischen Sättigungstypen und Fettsäureaufnahme durch Nahrung

```{r}
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$sat.i, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$sat.i, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)


pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$unsat.i, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$unsat.i, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)


pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$sat.i, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$sat.i, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$sat.i, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$sat.i, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$unsat.i, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$unsat.i, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$unsat.i, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$unsat.i, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)

```

Plotten der Unterschiede zwischen den Sättigungstypen

```{r}
FA_stool.ST$Time <- factor(FA_stool.ST$Time, levels = c("PRE", "POST"))

ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=sat.i)) + xlab('Phenotype') + ylab('Intake Saturated fatty acid Concentration [g]') +
  geom_boxplot(fill='whitesmoke', color='black') + 
  geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=unsat.i)) + xlab('Phenotype') + ylab('Intake unsaturated fatty acid Concentration [g]') +
  geom_boxplot(fill='whitesmoke', color='black') + 
  geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=sat.i)) + xlab('Time Point') + 
  ylab('Intake saturated fatty acid Concentration [g]') + 
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') + 
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)

ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=unsat.i)) + xlab('Time Point') + 
  ylab('Intake unaturated fatty acid Concentration [g]') + 
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') + 
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)


```

Bestimmen der HDL und LDL Konzentrationen und Ratio der Sättigungstypen

Plotten von Korrelation zwischen LDL und fäkalen gesättigten FA

```{r}
ggscatter(FA_stool.ST, x='sat', y='LDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL Concentration [mg/dl]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='sat', y='LDL', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL Concentration [mg/dl]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen HDL und fäkalen gesättigten FA

```{r}
ggscatter(FA_stool.ST, x='sat', y='HDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'HDL Concentration [mg/dl]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='sat', y='HDL', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'HDL Concentration [mg/dl]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen HDL/LDL-ratio und fäkalen gesättigten FA

```{r}
ggscatter(FA_stool.ST, x='sat', y='LDL_HDL_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL/HDL ratio')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='sat', y='LDL_HDL_ratio', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL/HDL ratio')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen LDL und fäkalen ungesättigten FA

```{r}
ggscatter(FA_stool.ST, x='unsat', y='LDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL Concentration [mg/dl]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='unsat', y='LDL', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL Concentration [mg/dl]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen HDL und fäkalen ungesättigten FA

```{r}
ggscatter(FA_stool.ST, x='unsat', y='HDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'HDL Concentration [mg/dl]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='unsat', y='HDL', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'HDL Concentration [mg/dl]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen HDL/LDL-ratio und fäkalen ungesättigten FA

```{r}
ggscatter(FA_stool.ST, x='unsat', y='LDL_HDL_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL/HDL ratio')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='unsat', y='LDL_HDL_ratio', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations fecal [nmol/g]', ylab = 'LDL/HDL ratio')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen LDL und aufgenommenen gesättigten FA

```{r}
ggscatter(FA_stool.ST, x='sat.i', y='LDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'LDL Concentration [mg/dl]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='sat.i', y='LDL', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'LDL Concentration [mg/dl]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen HDL und aufgenommenen gesättigten FA

```{r}
ggscatter(FA_stool.ST, x='sat.i', y='HDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'HDL Concentration [mg/dl]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='sat.i', y='HDL', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'HDL Concentration [mg/dl]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen LDL/HDL-ratio und aufgenommenen gesättigten FA

```{r}
ggscatter(FA_stool.ST, x='sat.i', y='LDL_HDL_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'LDL/HDL ratio')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='sat.i', y='LDL_HDL_ratio', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Saturated fatty acids concentrations intake [g]', ylab = 'LDL/HDL ratio')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen LDL und aufgenommenen ungesättigten FA
```{r}
ggscatter(FA_stool.ST, x='unsat.i', y='LDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'LDL Concentration [mg/dl]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='unsat.i', y='LDL', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'LDL Concentration [mg/dl]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen HDL und aufgenommenen ungesättigten FA

```{r}
ggscatter(FA_stool.ST, x='unsat.i', y='HDL',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'HDL Concentration [mg/dl]')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='unsat.i', y='HDL', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'HDL Concentration [mg/dl]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Plotten von Korrelationen zwischen LDL/HDL-ratio und aufgenommenen ungesättigten FA


```{r}
ggscatter(FA_stool.ST, x='unsat.i', y='LDL_HDL_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'), add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'LDL/HDL ratio')+
  facet_grid(.~ Time, scales="free")+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(FA_stool.ST, x='unsat.i', y='LDL_HDL_ratio', add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Unsaturated fatty acids concentrations intake [g]', ylab = 'LDL/HDL ratio')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

Wilcoxon-Test Unterschiede in LDL und HDL-Konzentrationen zwischen den Sättigungstypen

```{r}
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$LDL, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$LDL, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$HDL, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$HDL, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$LDL, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$LDL, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$LDL, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$LDL, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$HDL, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)
 
pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$HDL, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$HDL, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$HDL, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)


```

Plotten der Unterschiede

```{r}
FA_stool.ST$Time <- factor(FA_stool.ST$Time, levels = c("PRE", "POST"))

ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=LDL)) + xlab('Phenotype') + ylab('LDL concentration [mg/dl]') +
  geom_boxplot(fill='whitesmoke', color='black') + 
  geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=HDL)) + xlab('Phenotype') + ylab('HDL concentration [mg/dl]') +
  geom_boxplot(fill='whitesmoke', color='black') + 
  geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=LDL_HDL_ratio)) + xlab('Phenotype') + ylab('LDL/HDL ratio') +
  geom_boxplot(fill='whitesmoke', color='black') + 
  geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=LDL)) + xlab('Time Point') + 
  ylab('LDL concentration [mg/dl]') + 
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') + 
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)

ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=HDL)) + xlab('Time Point') + 
  ylab('HDL concentration [mg/dl]') + 
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') + 
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)


ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=LDL_HDL_ratio)) + xlab('Time Point') + 
  ylab('LDL/HDL ratio') + 
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') + 
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)

```

Testen der signifikanten Unterschiede der Fettsäurenausscheidung der Sättigungstypen
Laden und filtern der Metadaten

```{r}
FA_stool.ST <- read.table("/Users/student05/Documents/fa saturation mit intake types.txt", sep = '\t', comment='',
                       head=T)

View(FA_stool)

FA_stool.ST$Time <-factor(FA_stool.ST$Time, levels = c("PRE", "POST"))

row.names(FA_stool.ST) <- FA_stool.ST$SampleID

FA_stool.ST$Proband

FA_stool.ST <- subset(filter(FA_stool.ST, !SampleID == "ST.35AD.0U1"))

FA_stool.ST <- subset(filter(FA_stool.ST, !Proband == "33MP", !Proband == "35AD", !Proband == "34WF", !Proband == "49RJ"))

comparison_sat <- list(c("sat", "unsat"))

comparison_change <- list(c("change.sat", "change.unsat"))

comparison_time <- list(c("PRE", "POST"))
```


Wilcoxon Test Unterschiede fäkaler Fettsäurekonzentrationen zwischen Sättigungstypen

```{r}
pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$sat, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$sat, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "PRE"))$unsat, subset(filter(FA_stool.ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Time == "POST"))$unsat, subset(filter(FA_stool.ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "sat"))$sat, subset(filter(FA_stool.ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired =F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "unsat"))$sat, subset(filter(FA_stool.ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired= F)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.sat"))$sat, subset(filter(FA_stool.ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired =T)

pairwise.wilcox.test(subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$sat, subset(filter(FA_stool.ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired= F)

```

Plotten der Unterschiede

```{r}
ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=sat)) + xlab('Phenotype') + ylab('Saturated fatty acid Concentration [nmol/g DW]') +
  geom_boxplot(fill='whitesmoke', color='black') + 
  geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(subset(filter(FA_stool.ST)), aes(x=Phenotype, y=unsat)) + xlab('Phenotype') + ylab('Unsaturated fatty acid Concentration [nmol/g DW]') +
  geom_boxplot(fill='whitesmoke', color='black') + 
  geom_dotplot(binaxis= 'y', stackdir = 'center', dotsize = 0.3, fill = 'grey22', color='grey22') + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat) + facet_wrap(~Time)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=sat)) + xlab('Time Point') + 
  ylab('Saturated fatty acid Concentration [nmol/g DW]') + 
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') + 
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)

ggplot(subset(filter(FA_stool.ST)), aes(x=Time, y=unsat)) + xlab('Time Point') + 
  ylab('Unaturated fatty acid Concentration [nmol/g DW]') + 
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color= 'grey22') + 
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = F, aes(label= ..p.signif..), comparisons = comparison_time)
```

Bestimmen der means und SD

```{r}
mean(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "sat"))$sat) 

sd(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "sat"))$sat) 

mean(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "sat"))$sat) 

 sd(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "sat"))$sat)
 

mean(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "unsat"))$sat)

sd(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "unsat"))$sat)

mean(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "unsat"))$sat)

sd(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "unsat"))$sat)

mean(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "sat"))$unsat)

sd(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "sat"))$unsat)

mean(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "sat"))$unsat) 

sd(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "sat"))$unsat) 



mean(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "unsat"))$unsat) 

sd(subset(filter(FA_stool.ST, Time == "PRE" & Phenotype == "unsat"))$unsat) 

mean(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "unsat"))$unsat) 

sd(subset(filter(FA_stool.ST, Time == "POST" & Phenotype == "unsat"))$unsat)
```

Testen von Unterschiede im relativen Vorkommen der Taxa zwischen den Sättigungstypen
Laden der Phylum-Metadaten s.o.
Synchronisieren der Daten, hinzufügen von log-Transformation und Pseudocount 0.00001

```{r}
relab_phylum_ID <- relab_phylum_spread

relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))

row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID

relab_genus_ID <- relab_genus_spread

relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))

row.names(relab_genus_ID) <- relab_genus_ID$SampleID


FA_stool <- subset(filter(FA_stool, !Proband == "31KE", !Proband == "34WF",
                          
                          !Proband == "45GL", !Proband == "49RJ", !Proband == "54SL", !Proband == "74SA"))

FA_stool.ST <- mutate(FA_stool.ST, SampleID1 = paste(Proband, Time, sep = "."))

row.names(FA_stool.ST) <- FA_stool.ST$SampleID1

common.ids.relab <- intersect(rownames(FA_stool.ST), rownames(relab_phylum_ID))

FA_stool.ST <- FA_stool.ST[common.ids.relab,]

relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]

relab_phylum_ID1 <- relab_phylum_ID[,c(3:8)] + 0.00001

relab_phylum_ID_log <- log10(relab_phylum_ID_log)


phylum_ST <- cbind(relab_phylum_ID1, FA_stool.ST)

write.table(phylum_ST, file = '/Users/student05/Documents/fa feces/FA fecal/saturation types/phylum-phenotype.txt', sep = "\t", col.names = TRUE,row.names = FALSE)

```
Wilcoxon-Test zur Bestimmung von Unterschiede im relativen Vorkommen der Phyla zwischen Sättigungstypen

Firmicutes

```{r}
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)


pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)


ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Firmicutes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Firmicutes)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Firmicutes)) + xlab('Time') + ylab('log10 (Relative Abundance p__Firmicutes)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)


```

Actinobacteria

```{r}

pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Actinobacteria, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
 
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Actinobacteria, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)


ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Actinobacteria)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Actinobacteria)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Actinobacteria)) + xlab('Time') + ylab('log10 (Relative Abundance p__Actinobacteria)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
phylum_ST$k__Bacteria.p__Actinobacteria

```

Bacteroidetes
In Arbeit

```{r}

mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes) 


mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes)

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes) 



mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes)


mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes)




pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.sat"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Bacteroidetes, subset(filter(phylum_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)

library(scales)

pdf("/Users/student05/Documents/fertige Plots/sat.types.bacteroidetes.neu.pdf",width=8, height=10)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes, fill= Phenotype)) + 
  xlab('Phenotype') + ylab('Relatives Vorkommen p__Bacteroidetes [%]') +
  geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
  scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
  scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
                    values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=45, hjust=1))+
  scale_y_log10(labels = percent_format())
dev.off()

ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Bacteroidetes)) + xlab('Time') + ylab('log10 (Relative Abundance p__Bacteroidetes)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)


```

Proteobacteria

```{r}
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Proteobacteria, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Proteobacteria, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)


ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Proteobacteria)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Proteobacteria)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Proteobacteria)) + xlab('Time') + ylab('log10 (Relative Abundance p__Proteobacteria)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)

```


Tenericutes

```{r}

pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Tenericutes, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
 
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Tenericutes, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Tenericutes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Tenericutes)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Tenericutes)) + xlab('Time') + ylab('log10 (Relative Abundance p__Tenericutes)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)

```

Verrucomicrobia
In Arbeit

```{r}

mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia) 


mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia) 



mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia)


mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia)

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia)



pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
 
pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Verrucomicrobia)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Verrucomicrobia)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  scale_y_log10(labels = percent_format())

ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Bacteroidetes)) + xlab('Phenotype') + ylab('log10 (Relative Abundance p__Bacteroidetes)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1)) +
  scale_y_log10(labels = percent_format())


ggplot(subset(filter(phylum_ST)), aes(x=Time,y=k__Bacteria.p__Verrucomicrobia)) + xlab('Time') + ylab('log10 (Relative Abundance p__Verrucomicrobia)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)

pdf("/Users/student05/Documents/fertige Plots/sat.types.verrucomicrobia.neu.pdf",width=8, height=10)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=k__Bacteria.p__Verrucomicrobia, fill= Phenotype)) + 
  xlab('Phenotype') + ylab('Relatives Vorkommen p__Verrucomicrobia [%]') +
  geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
  scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
  scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
                    values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = )+
  theme(legend.position="none")+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=45, hjust=1))+
  scale_y_log10(labels = percent_format())
dev.off()

```

Laden der Genus-Metadaten s.o.
Synchronisieren der Daten, hinzufügen von log-Transformation und Pseudocount 0.00001

```{r}
common.ids.relab <- intersect(rownames(FA_stool.ST), rownames(relab_genus_ID))

FA_stool.ST <- FA_stool.ST[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]

relab_genus_ID1 <- relab_genus_ID[,c(3:31)] + 0.00001

relab_genus_ID_log <- log10(relab_genus_ID_log)


genus_ST <- cbind(relab_genus_ID1, FA_stool.ST)
```

Wilcoxon-Test zur Bestimmung von Unterschiede im relativen Vorkommen der Gattungen zwischen Sättigungstypen

Bifidobacterium

```{r}

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)

ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Bifidobacterium)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium)) + xlab('Time') + ylab('log10 (Relative Abundance g__Bifidobacterium)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)


```


Faecalibacterium

```{r}

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)

ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Faecalibacterium)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  scale_y_log10(labels = percent_format())


ggplot(subset(filter(genus_ST)), aes(x=Time,y=10^k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium)) + xlab('Time') + ylab('log10 (Relative Abundance g__Faecalibacterium)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)+
  scale_y_log10(labels = percent_format())

```

Sutterella

```{r}

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)
pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)

ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Sutterella)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella)) + xlab('Time') + ylab('log10 (Relative Abundance g__Sutterella)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)

```

Oscillospira
In Arbeit

```{r}

mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)


mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 



mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)


mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)

mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira) 

sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)


pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "change.sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(genus_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(genus_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)

ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Oscillospira)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  scale_y_log10(labels = percent_format())


pdf("/Users/student05/Documents/fertige Plots/sat.types.oscillo.neu.pdf",width=8, height=10)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira, fill= Phenotype)) + 
  xlab('Phänotyp') + ylab('Relatives Vorkommen g__Oscillospira [%]') +
  geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
  scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
  scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
                    values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=45, hjust=1))+
  scale_y_log10(labels = percent_format())
dev.off()

ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira)) + xlab('Time') + ylab('log10 (Relative Abundance g__Oscillospira)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)


```

Akkermansia
In Arbeit

```{r}

mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 


mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 



mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)  

mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)


mean(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

sd(subset(filter(genus_ST, Time == "PRE" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

mean(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia) 

sd(subset(filter(genus_ST, Time == "POST" & Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)


pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "change.sat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(genus_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "change.unsat"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(genus_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)

ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=10^k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Akkermansia)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  scale_y_log10(labels = percent_format())

pdf("/Users/student05/Documents/fertige Plots/sat.types.akkermansia.neu.pdf",width=8, height=10)
ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia, fill= Phenotype)) + 
  xlab('Phänotyp') + ylab('Relatives Vorkommen g__Akkermansia [%]') +
  geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
  scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
  scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
                    values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = )+
  theme(legend.position="none")+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=45, hjust=1))+
  scale_y_log10(labels = percent_format())
dev.off()

ggplot(subset(filter(genus_ST)), aes(x=Time,y=10^k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia)) + xlab('Time') + ylab('log10 (Relative Abundance g__Akkermansia)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)+
  scale_y_log10(labels = percent_format())

```

Bacteroides

```{r}

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)


ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Bacteroides)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides)) + xlab('Time') + ylab('log10 (Relative Abundance g__Bacteroides)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)

```

Prevotella

```{r}

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)

ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Prevotella)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella)) + xlab('Time') + ylab('log10 (Relative Abundance g__Prevotella)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)

```

Dorea

```{r}
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)

ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Dorea)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea)) + xlab('Time') + ylab('log10 (Relative Abundance g__Dorea)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)

```

Collinsella

```{r}

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)


ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella)) + xlab('Phenotype') + ylab('log10 (Relative Abundance g__Collinsella)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella)) + xlab('Time') + ylab('log10 (Relative Abundance g__Collinsella)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)

```

Rikenellaceae

```{r}
pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "sat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, subset(filter(genus_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Phenotype == "unsat"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, subset(filter(genus_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "PRE"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, subset(filter(genus_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = FALSE)

pairwise.wilcox.test(subset(filter(genus_ST, Time == "POST"))$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__, subset(filter(genus_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired=FALSE)

ggplot(subset(filter(genus_ST)), aes(x=Phenotype,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__)) + xlab('Phenotype') + ylab('log10 (Relative Abundance f__Rikenellaceae)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)

ggplot(subset(filter(genus_ST)), aes(x=Time,y=k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__)) + xlab('Time') + ylab('log10 (Relative Abundance f__Rikenellaceae)') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)



FA_stool.PRE <- subset(filter(FA_stool.ST, Time == "PRE"))

FA_stool.POST <- subset(filter(FA_stool.ST, Time == "POST"))

```

Unterschiede im Firmicutes/Bacteroidetes-ratio zwischen den Sättigungstypen

Laden der Metadaten, Bestimmen von Mean und SD

```{r}

phylum_ST <- read.table("/Users/student05/Documents/fa feces/FA fecal/saturation types/phylum.phenotype.txt", sep ='\t', comment='', head=T)

phylum_ST$Time <-factor(phylum_ST$Time, levels = c("PRE", "POST"))


mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$F_B_ratio) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "sat"))$F_B_ratio)

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$F_B_ratio) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "sat"))$F_B_ratio) 


mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$F_B_ratio) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.sat"))$F_B_ratio) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$F_B_ratio) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.sat"))$F_B_ratio) 



mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$F_B_ratio) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "unsat"))$F_B_ratio)  

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$F_B_ratio) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "unsat"))$F_B_ratio)


mean(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$F_B_ratio) 

sd(subset(filter(phylum_ST, Time == "PRE" & Phenotype == "change.unsat"))$F_B_ratio) 

mean(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$F_B_ratio) 

sd(subset(filter(phylum_ST, Time == "POST" & Phenotype == "change.unsat"))$F_B_ratio)

```

Wilcoxon-Test zur Bestimmung von Unterschiede im F/B-ratio zwischen Sättigungstypen

In Arbeit

```{r}

pairwise.wilcox.test(subset(filter(phylum_ST, Time == "PRE"))$F_B_ratio, subset(filter(phylum_ST, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Time == "POST"))$F_B_ratio, subset(filter(phylum_ST, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.sat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "change.sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "change.unsat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "change.unsat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "sat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "sat"))$Time, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(phylum_ST, Phenotype == "unsat"))$F_B_ratio, subset(filter(phylum_ST, Phenotype == "unsat"))$Time, p.adjust.method = 'BH', paired = F)

ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=F_B_ratio)) + xlab('Phenotype') + ylab('Firmicutes/Bacteroidetes ratio') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))

pdf("/Users/student05/Documents/fertige Plots/sat.types.F.B.pdf",width=8, height=10)
ggplot(subset(filter(phylum_ST)), aes(x=Phenotype,y=F_B_ratio, fill= Phenotype)) + 
  xlab('Phenotype') + ylab('Firmicutes/Bacteroidetes Verhältnis') +
  geom_boxplot(width = .7, lwd=0.6) + theme_classic()+
  scale_x_discrete(labels=c("change.sat" = "zu gesättigt", "change.unsat" = "zu ungesättigt", "sat" = "gesättigt", "unsat"= "ungesättigt"))+
  scale_fill_manual(labels = c("wechsel zu gesättigt", "wechsel zu ungesättigt", "gesättigt", "ungesättigt"),
                    values = c("lightskyblue4", "lightskyblue3", "lightskyblue2", "lightskyblue"))+
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_sat)+
  theme(legend.position="none")+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=45, hjust=1))
dev.off()

types <- melt(phylum_ST, id.vars = c('Time', 'F_B_ratio'), measure.vars = c('sat', 'unsat'))

types.pr <- subset(filter(types, !Time == 'POST'))

types.po <- subset(filter(types, !Time == 'PRE'))

types <-dplyr::rename(types, FA=variable)
                      
types <- dplyr::rename(types, Concentration=value)


pairwise.wilcox.test(subset(filter(convT, Time == "PRE"))$F_B_ratio, subset(filter(convT, Time == "PRE"))$Phenotype2, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(convT, Time == "POST"))$F_B_ratio, subset(filter(convT, Time == "POST"))$Phenotype2, p.adjust.method = 'BH', paired = F)


ggscatter(types.pr, x='Concentration', y='F_B_ratio',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ FA)+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
theme(plot.title = element_text(color="black", size=14))


phylum_ST$Time <- factor(phylum_ST$Time, levels = c("PRE", "POST"))

pdf("/Users/student05/Documents/fertige Plots/sat.bact.firm.pdf",width=8, height=10)
ggscatter(phylum_ST, x='sat', y='F_B_ratio',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', size=2.5,cor.coef.coord =c(250, 19),cor.coef.size = 7,conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Firmicutes/Bacteroidetes Verhältnis')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")
dev.off()


pdf("/Users/student05/Documents/fertige Plots/unsat.bact.firm.pdf",width=8, height=10)
ggscatter(phylum_ST, x='unsat', y='F_B_ratio',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line',size=2.5, cor.coef.coord =c(250, 19),cor.coef.size = 7,conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Firmicutes/Bacteroidetes Verhältnis')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")
dev.off()


ggscatter(types.po, x='Concentration', y='F_B_ratio',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, 6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ FA)+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(types, x='Concentration', y='F_B_ratio',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, 16),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ FA)+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))



```

5. Analysen mit dem Firmicutes/Bacteroidetes-ratio
Unterschiede im F/B-ratio zwischen Sterolkonvertierungstypen
Unterteilen in high und low converter

```{r}
lowconv <- filter(phylum_ST, Proband == "05AP" | Proband == "33MP"
                  
                  | Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
                  
                  | Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")

lowconv['Phenotype2'] = 'low converter'

highconv <- filter(phylum_ST, Proband == "06WT" | Proband == "07RW"
                   
                   | Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
                   
                   | Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
                   
                   | Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
                   
                   | Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
                   
                   | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")

highconv['Phenotype2'] = 'high converter'

highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL

noconv <- filter(phylum_ST, Proband == "28HM" | Proband == "32FG"
                 
                 | Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
                 
                 | Proband == "39DA" | Proband == "66DG" | Proband == "70PL")

noconv['Phenotype2'] = 'not classified'

noconv$Converter.Type <- NULL

convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)

comparison_conv <- list(c("low converter", "high converter"))

convT <- subset(filter(convT, !Phenotype2 == 'not classified'))

pairwise.wilcox.test(subset(filter(convT, Time == "PRE"))$F_B_ratio, subset(filter(convT, Time == "PRE"))$Phenotype2, p.adjust.method = 'BH', paired = F)

pairwise.wilcox.test(subset(filter(convT, Time == "POST"))$F_B_ratio, subset(filter(convT, Time == "POST"))$Phenotype2, p.adjust.method = 'BH', paired = F)

ggplot(subset(filter(convT)), aes(x=Phenotype2,y=F_B_ratio)) + xlab('Phenotype') + ylab('Firmicutes/Bacteroidetes ratio') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Time) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_conv)+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))


ggplot(subset(filter(convT)), aes(x=Time,y=F_B_ratio)) + xlab('Time') + ylab('Firmicutes/Bacteroidetes ratio') +
  geom_boxplot(fill = 'whitesmoke', color="black") + 
  geom_dotplot(binaxis = 'y', stackdir = 'center', dotsize = 0.2, fill = 'grey22', color = 'grey22') +
  facet_wrap(~Phenotype2) + 
  stat_compare_means(paired = FALSE, aes(label = ..p.signif..), comparisons = comparison_time)
```

Korrelationsanalysen zwischen allen fäkalen Fettsäuren und dem F/B-ratio
Loop und Plots zu gesättigte Fettsäuren und F/B-ratio
In Arbeit

```{r}

phylum_colnames <- colnames(phylum_ST[, c(3:9)])

corr_map_phylum_sat <- filter(phylum_ST, !is.na(sat))

corr_spearman_Phylum_sat <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_sat, !is.na(i))

  y = tmp[,i]
  
  x = tmp$sat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$sat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$sat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_sat)+1

  corr_spearman_Phylum_sat[nrow,"FA"] <- "sat"
  
  corr_spearman_Phylum_sat[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_sat[nrow, "p.value"] = p
  
  corr_spearman_Phylum_sat[nrow, "rho"] = rho
  
  corr_spearman_Phylum_sat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_sat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_sat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_sat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_sat$p.adjusted <- p.adjust(corr_spearman_Phylum_sat$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_sat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_sat$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_sat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_sat$p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_sat <- filter(corr_spearman_Phylum_sat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_sat, file = '/Users/student05/Documents/FB q-value/sat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_ST, x='sat', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'saturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='sat', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'saturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
 
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

pdf("/Users/student05/Documents/fertige Plots/sat.bact.firm.pdf",width=8, height=10)
ggscatter(phylum_ST, x='sat', y='F_B_ratio',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', cor.coef.coord =c(250, 19),cor.coef.size = 7,conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Gesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'Firmicutes/Bacteroidetes Verhältnis')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")
dev.off()

```

Loop und Plots zu ungesättigte Fettsäuren und F/B-ratio

```{r}

phylum_colnames <- colnames(phylum_ST[, c(3:9)])

corr_map_phylum_unsat <- filter(phylum_ST, !is.na(unsat))

corr_spearman_Phylum_unsat <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_unsat, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$unsat

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_unsat)+1
 
  corr_spearman_Phylum_unsat[nrow,"FA"] <- "unsat"
  
  corr_spearman_Phylum_unsat[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_unsat[nrow, "p.value"] = p
  
  corr_spearman_Phylum_unsat[nrow, "rho"] = rho
  
  corr_spearman_Phylum_unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_unsat$p.adjusted <- p.adjust(corr_spearman_Phylum_unsat$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_unsat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_unsat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_unsat$p.value_POST, method = "BH", n = 35)


corr_sig_Phylum_unsat <- filter(corr_spearman_Phylum_unsat, p.adjusted < 0.05 | p.adjusted_PRE < 0.05 | p.adjusted_POST < 0.05)

write.table(corr_spearman_Phylum_unsat, file = '/Users/student05/Documents/FB q-value/unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plots zu einfach ungesättigten FA und F/B-ratio 

```{r}
ggscatter(phylum_ST, x='mono.unsat', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Monounsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='mono.unsat', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Monounsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")
```

Plots zu zweifach ungesättigten FA - total FA und F/B-ratio 

```{r}

ggscatter(phylum_ST, x='di.unsat', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Diunsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='di.unsat', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Diunsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='more.2.unsat', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='more.2.unsat', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='less.14', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '< 14 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='more.2.unsat', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '> 2 unsaturated fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='c14.17', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= ' 14-17 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='c14.17', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '> 14-17 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='c18.19', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= ' 18-19 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='c18.19', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '18-19 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='c20.21', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= ' 20-21 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='c20.21', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '20-21 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='c22.24', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= ' 22-24 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='c20.21', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= '20-21 c atomes fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='total', y='F_B_ratio',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= ' total fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

ggscatter(phylum_ST, x='total', y='F_B_ratio',  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'total fatty acid concentration [nmol/g]', ylab = 'Firmicutes/Bacteroidetes ratio')+
  
  theme(strip.text.x = element_text(size = 10, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none") 

```

5.2 Korrelationen gesättigte FA und ungesättigte FA mit phylum-level

Filtern der Phylum-Metadaten, hinzufügen von log-Transformation und Pseudocount 0.0001

```{r}

phylum_ST_log <- phylum_ST[,c(3:8)] + 0.00001

phylum_ST_log <- log10(phylum_ST_log)

phylum_ST <- cbind(phylum_ST_log, phylum_ST[, c(9:33)])

phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])



corr_map_phylum_unsat <- filter(phylum_ST, !is.na(unsat))

corr_spearman_Phylum_unsat <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_unsat, !is.na(i))

  y = tmp[,i]
  
  x = tmp$unsat

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time.1 == "PRE"))[,i]
  
  w = subset(filter(tmp, Time.1 == "PRE"))$unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time.1 == "POST"))[,i]
  
  s = subset(filter(tmp, Time.1 == "POST"))$unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_unsat)+1
  
  corr_spearman_Phylum_unsat[nrow,"FA"] <- "unsat"
  
  corr_spearman_Phylum_unsat[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_unsat[nrow, "p.value"] = p
  
  corr_spearman_Phylum_unsat[nrow, "rho"] = rho
  
  corr_spearman_Phylum_unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_unsat$p.adjusted <- p.adjust(corr_spearman_Phylum_unsat$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_unsat$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_unsat$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_unsat$p.value_POST, method = "BH", n = 35)



write.table(corr_spearman_Phylum_unsat, file = '/Users/student05/Documents/unsat.phylum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


```

Plotten der Korrelationen zwischen gesättigten und ungesättigten FA und phylum-level

Teilweise in Arbeit

```{r}

phylum_ST$k__Bacteria.p__Firmicutes

melt.Fi <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Firmicutes'), measure.vars = c('sat', 'unsat'))

melt.Fi<-dplyr::rename(melt.Fi, FA=variable)
melt.Fi <- dplyr::rename(melt.Fi, Concentration=value)

melt.Fi.pr <- subset(filter(melt.Fi, !Time.1 == 'POST'))

melt.Fi.po <- subset(filter(melt.Fi, !Time.1 == 'PRE'))

melt.Fi.pr$k__Bacteria.p__Firmicutes

ggscatter(melt.Fi.pr, x='Concentration', y='k__Bacteria.p__Firmicutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -0.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Firmicutes')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Fi.po, x='Concentration', y='k__Bacteria.p__Firmicutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.75),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Firmicutes')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Fi, x='Concentration', y='k__Bacteria.p__Firmicutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.7),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Firmicutes')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))





phylum_ST$k__Bacteria.p__Bacteroidetes

melt.Ba <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Bacteroidetes'), measure.vars = c('sat', 'unsat'))

melt.Ba<-dplyr::rename(melt.Ba, FA=variable)
melt.Ba <- dplyr::rename(melt.Ba, Concentration=value)

melt.Ba.pr <- subset(filter(melt.Ba, !Time.1 == 'POST'))

melt.Ba.po <- subset(filter(melt.Ba, !Time.1 == 'PRE'))

ggscatter(melt.Ba.pr, x='Concentration', y='k__Bacteria.p__Bacteroidetes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -0.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Bacteroidetes')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))


ggscatter(melt.Ba.po, x='Concentration', y='k__Bacteria.p__Bacteroidetes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.95),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Bacteroidetes')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Ba, x='Concentration', y='k__Bacteria.p__Bacteroidetes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.9),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Bacteroidetes')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))

phylum_ST$Time.1 <- factor(phylum_ST$Time.1, levels = c("PRE", "POST"))

pdf("/Users/student05/Documents/fertige Plots/unsat.bacteroidetes.pdf",width=8, height=10)
ggscatter(phylum_ST, x='unsat', y='k__Bacteria.p__Bacteroidetes',color = 'Time.1', palette = c('skyblue', 'orchid'),  add = 'reg.line', cor.coef.coord =c(0, -0.8),cor.coef.size = 7,conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'log10 (Relatives Vorkommen p__Bacteroidetes)')+
  facet_grid(.~ Time.1, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=45, hjust=1))+
  theme(legend.position="none")
dev.off()




phylum_ST$k__Bacteria.p__Verrucomicrobia

melt.Ve <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Verrucomicrobia'), measure.vars = c('sat', 'unsat'))

melt.Ve<-dplyr::rename(melt.Ve, FA=variable)
melt.Ve <- dplyr::rename(melt.Ve, Concentration=value)

melt.Ve.pr <- subset(filter(melt.Ve, !Time.1 == 'POST'))

melt.Ve.po <- subset(filter(melt.Ve, !Time.1 == 'PRE'))


ggscatter(melt.Ve.pr, x='Concentration', y='k__Bacteria.p__Verrucomicrobia',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -0.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Verrucomicrobia')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Ve.po, x='Concentration', y='k__Bacteria.p__Verrucomicrobia',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -0.3),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Verrucomicrobia')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Ve, x='Concentration', y='k__Bacteria.p__Verrucomicrobia',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Verrucomicrobia')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))





phylum_ST$k__Bacteria.p__Tenericutes

melt.Te <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Tenericutes'), measure.vars = c('sat', 'unsat'))

melt.Te<-dplyr::rename(melt.Te, FA=variable)
melt.Te <- dplyr::rename(melt.Te, Concentration=value)

melt.Te.pr <- subset(filter(melt.Te, !Time.1 == 'POST'))

melt.Te.po <- subset(filter(melt.Te, !Time.1 == 'PRE'))


ggscatter(melt.Te.pr, x='Concentration', y='k__Bacteria.p__Tenericutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(350, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Tenericutes')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))


ggscatter(melt.Te.po, x='Concentration', y='k__Bacteria.p__Tenericutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(600, -1.3),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Tenericutes')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Te, x='Concentration', y='k__Bacteria.p__Tenericutes',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Tenericutes')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))





phylum_ST$k__Bacteria.p__Actinobacteria

melt.Ac <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Actinobacteria'), measure.vars = c('sat', 'unsat'))

melt.Ac<-dplyr::rename(melt.Ac, FA=variable)
melt.Ac <- dplyr::rename(melt.Ac, Concentration=value)
t

melt.Ac.pr <- subset(filter(melt.Ac, !Time.1 == 'POST'))

melt.Ac.po <- subset(filter(melt.Ac, !Time.1 == 'PRE'))

ggscatter(melt.Ac.pr, x='Concentration', y='k__Bacteria.p__Actinobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Actinobacteria')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))


ggscatter(melt.Ac.po, x='Concentration', y='k__Bacteria.p__Actinobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -1.4),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Actinobacteria')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Ac, x='Concentration', y='k__Bacteria.p__Actinobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.35),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Actinobacteria')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))




phylum_ST$k__Bacteria.p__Proteobacteria

melt.Pr <- melt(phylum_ST, id.vars = c('Time.1', 'k__Bacteria.p__Proteobacteria'), measure.vars = c('sat', 'unsat'))


melt.Pr<-dplyr::rename(melt.Pr, FA=variable)
melt.Pr <- dplyr::rename(melt.Pr, Concentration=value)

melt.Pr.pr <- subset(filter(melt.Pr, !Time.1 == 'POST'))

melt.Pr.po <- subset(filter(melt.Pr, !Time.1 == 'PRE'))


ggscatter(melt.Pr.pr, x='Concentration', y='k__Bacteria.p__Proteobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.6),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Proteobacteria')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Pr.po, x='Concentration', y='k__Bacteria.p__Proteobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(500, -1.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Proteobacteria')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Pr, x='Concentration', y='k__Bacteria.p__Proteobacteria',color = 'FA', palette = c('tomato', 'yellowgreen', 'steelblue2', 'deeppink2', 'cyan','yellow'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.35),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance p__Proteobacteria')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))

```

5.3 Korrelationen gesättigte FA und ungesättigte FA mit genus-level

Filtern der Genus-Metadaten, hinzufügen von log-Transformation und Pseudocount 0.0001
Loop Genus-level


```{r}

FA_stool.ST <- mutate(FA_stool.ST, SampleID1 = paste(Proband, Time, sep = "."))

row.names(FA_stool.ST) <- FA_stool.ST$SampleID1

genus_colnames <- colnames(relab_genus_spread[, c(3:31)])

common.ids.relab <- intersect(rownames(FA_stool.ST), rownames(relab_phylum_ID))

FA_stool.ST <- FA_stool.ST[common.ids.relab,]

relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]

relab_genus_ID_log <- relab_genus_ID[,c(3:31)] + 0.00001

relab_genus_ID_log <- log10(relab_genus_ID_log)

genus_FA <- cbind(relab_genus_ID_log, FA_stool.ST)



corr_map_genus_unsat <- filter(genus_FA, !is.na(unsat))

corr_spearman_genus_unsat <- data.frame()

for( i in genus_colnames) {
  
  tmp <- filter(corr_map_genus_unsat, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$unsat
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$unsat
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$unsat
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_genus_unsat)+1
  
  corr_spearman_genus_unsat[nrow,"FA"] = "unsaturated"
  
  corr_spearman_genus_unsat[nrow, "Genus"] = i
  
  corr_spearman_genus_unsat[nrow, "p.value"] = p
  
  corr_spearman_genus_unsat[nrow, "rho"] = rho
  
  corr_spearman_genus_unsat[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_genus_unsat[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_genus_unsat[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_genus_unsat[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_genus_unsat$p.adjusted <- p.adjust(corr_spearman_genus_unsat$p.value, method = "BH", n = 35)

corr_spearman_genus_unsat$p.adjusted_PRE <- p.adjust(corr_spearman_genus_unsat$p.value_PRE, method = "BH", n = 35)

corr_spearman_genus_unsat$p.adjusted_POST <- p.adjust(corr_spearman_genus_unsat$p.value_POST, method = "BH", n = 35)

write.table(corr_spearman_genus_unsat, file = '/Users/student05/Documents/unsat.genus.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten der Korrelationen zwischen gesättigten und ungesättigten FA und genus-level

Teilweise in Arbeit

```{r}

genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira

pdf("/Users/student05/Documents/fertige Plots/unsat.oscillo.pdf",width=8, height=10)
ggscatter(genus_FA, x='unsat', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', cor.coef.coord =c(0, -2),cor.coef.size = 7,conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Ungesättigte Fettsäurenkonzentrationen [nmol/g]', ylab = 'log10 (Relatives Vorkommen g__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")
dev.off

melt.Os <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira'), measure.vars = c('sat', 'unsat'))
melt.Os<-dplyr::rename(melt.Os, FA=variable)
melt.Os <- dplyr::rename(melt.Os, Concentration=value)

melt.Os.pr <- subset(filter(melt.Os, !Time == 'POST'))

melt.Os.po <- subset(filter(melt.Os, !Time == 'PRE'))

ggscatter(melt.Os.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -2),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Oscillospira')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Os.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -2),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Oscillospira')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Os, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -2),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Oscillospira')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))






genus_FA$k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium

melt.Bi <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium'), measure.vars = c('sat', 'unsat'))

melt.Bi<-dplyr::rename(melt.Bi, FA=variable)
melt.Bi <- dplyr::rename(melt.Bi, Concentration=value)

melt.Bi.pr <- subset(filter(melt.Bi, !Time == 'POST'))

melt.Bi.po <- subset(filter(melt.Bi, !Time == 'PRE'))

ggscatter(melt.Bi.pr, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bifidobacterium')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Bi.po, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bifidobacterium')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Bi, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.3),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bifidobacterium')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))





genus_FA$k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella

melt.Co <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella'), measure.vars = c('sat', 'unsat'))

melt.Co<-dplyr::rename(melt.Co, FA=variable)
melt.Co <- dplyr::rename(melt.Co, Concentration=value)

melt.Co.pr <- subset(filter(melt.Co, !Time == 'POST'))

melt.Co.po <- subset(filter(melt.Co, !Time == 'PRE'))

ggscatter(melt.Co.pr, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Collinsella')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Co.po, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Collinsella')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Co, x='Concentration', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Collinsella')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))





genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium

melt.Fa <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium'), measure.vars = c('sat', 'unsat'))

melt.Fa<-dplyr::rename(melt.Fa, FA=variable)
melt.Fa <- dplyr::rename(melt.Fa, Concentration=value)

melt.Fa.pr <- subset(filter(melt.Fa, !Time == 'POST'))

melt.Fa.po <- subset(filter(melt.Fa, !Time == 'PRE'))


ggscatter(melt.Fa.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Fa.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Fa, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Faecalibacterium')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))






genus_FA$k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia

melt.Ak <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia'), measure.vars = c('sat', 'unsat'))

melt.Ak<-dplyr::rename(melt.Ak, FA=variable)
melt.Ak <- dplyr::rename(melt.Ak, Concentration=value)

melt.Ak.pr <- subset(filter(melt.Ak, !Time == 'POST'))

melt.Ak.po <- subset(filter(melt.Ak, !Time == 'PRE'))


ggscatter(melt.Ak.pr, x='Concentration', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.3),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Akkermansia')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Ak.po, x='Concentration', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Akkermansia')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Ak, x='Concentration', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Akkermansia')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))




genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia

melt.Bl <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia'), measure.vars = c('sat', 'unsat'))

melt.Bl<-dplyr::rename(melt.Bl, FA=variable)
melt.Bl <- dplyr::rename(melt.Bl, Concentration=value)

melt.Bl.pr <- subset(filter(melt.Bl, !Time == 'POST'))

melt.Bl.po <- subset(filter(melt.Bl, !Time == 'PRE'))

ggscatter(melt.Bl.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(300, -1.7),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Blautia')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Bl.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Blautia')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Bl, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Blautia',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Blautia')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))




genus_FA$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides

melt.Bc <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides'), measure.vars = c('sat', 'unsat'))

melt.Bc<-dplyr::rename(melt.Bc, FA=variable)
melt.Bc <- dplyr::rename(melt.Bc, Concentration=value)

melt.Bc.pr <- subset(filter(melt.Bc, !Time == 'POST'))

melt.Bc.po <- subset(filter(melt.Bc, !Time == 'PRE'))

ggscatter(melt.Bc.pr, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(200, -0.8),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bacteroides')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Bc.po, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.2),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bacteroides')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Bc, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Bacteroides')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))





genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus

melt.Cp <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus'), measure.vars = c('sat', 'unsat'))

melt.Cp<-dplyr::rename(melt.Cp, FA=variable)
melt.Cp <- dplyr::rename(melt.Cp, Concentration=value)

melt.Cp.pr <- subset(filter(melt.Cp, !Time == 'POST'))

melt.Cp.po <- subset(filter(melt.Cp, !Time == 'PRE'))

ggscatter(melt.Cp.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(200, -1.6),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Coprococcus')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Cp.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Coprococcus')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Cp, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.5),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Coprococcus')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))




genus_FA$k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.

melt.Ru <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.'), measure.vars = c('sat', 'unsat'))

melt.Ru<-dplyr::rename(melt.Ru, FA=variable)
melt.Ru <- dplyr::rename(melt.Ru, Concentration=value)

melt.Ru.pr <- subset(filter(melt.Ru, !Time == 'POST'))

melt.Ru.po <- subset(filter(melt.Ru, !Time == 'PRE'))


ggscatter(melt.Ru.pr, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(200, -2),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Ruminococcus')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Ru.po, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -2),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Ruminococcus')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Ru, x='Concentration', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -1.9),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Ruminococcus')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))





genus_FA$k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella

melt.Pe <- melt(genus_FA, id.vars = c('Time', 'k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella'), measure.vars = c('sat', 'unsat'))


melt.Pe<-dplyr::rename(melt.Pe, FA=variable)
melt.Pe <- dplyr::rename(melt.Pe, Concentration=value)

melt.Pe.pr <- subset(filter(melt.Pe, !Time == 'POST'))

melt.Pe.po <- subset(filter(melt.Pe, !Time == 'PRE'))


ggscatter(melt.Pe.pr, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(200, -0.6),cor.coef.size = 6, xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Prevotella')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('PRE')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Pe.po, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Prevotella')+
  facet_grid(.~ FA, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('POST')+
  theme(plot.title = element_text(color="black", size=14))

ggscatter(melt.Pe, x='Concentration', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'FA', palette = c('steelblue2', 'deeppink2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(300, -0.6),cor.coef.size = 6,xlab= 'Fatty acid concentration [nmol/g]', ylab = 'Relative Abundance g__Prevotella')+
  facet_grid(.~ FA,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")+
  ggtitle('Times together')+
  theme(plot.title = element_text(color="black", size=14))

```

6. Serumlipidanalyse

Laden der Daten und testen auf Normalverteilung

```{r}

LI_serum <- read.table("/Users/student05/Documents/serum lipids zahlen.1-2.txt", sep = '\t', comment='',head=T)

LI_serum$Time <-factor(LI_serum$Time, levels = c("PRE", "POST"))

row.names(LI_serum) <- LI_serum$SampleID                       

nd.LI<- data_frame()

for (i in LI_colnames)  {
  fit <- shapiro.test(as.matrix(as.data.frame(lapply(LI_serum[,i],as.numeric))))
  p = fit$p.value
  nrow = nrow(nd.LI)+1
  nd.LI[nrow, "column"] = i
  nd.LI[nrow, "p.value"] = round(p, 4)
}


ggqqplot(LI_serum$Sum, ylab = "Sum serum lipids [nmol/ml]", xlab = "SampleID")
ggqqplot(LI_serum$PE, ylab = "Phosphatidylethanolamine
 serum lipids [nmol/ml]", xlab = "SampleID")

```

Alle Probanden zeigen PRE und POST Proben, Follow-up nicht vorhanden

Loop für Wilcoxon-Test zwischend den Zeitpunkten PRE und POST

```{r}

wilcox_LI<- data_frame()

for (i in LI_colnames) {
  
  tmp <- LI_serum %>% drop_na(i) 
  
  x <- as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))
  
  y <- LI_serum$Time 
  
  tmp_wilcox <- pairwise.wilcox.test(x, y, p.adjust.method = 'BH', paired = T)
  
  p <- tmp_wilcox$p.value
  
  nrow = nrow(wilcox_LI)+1
  
  wilcox_LI[nrow, "LI"] <- i 
  
  
  wilcox_LI[nrow, "Mean PRE"] <-round(mean(subset(filter(LI_serum,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), 2, mean,  na.rm = TRUE), 4)
  
  wilcox_LI[nrow, "sd PRE"] <-round(sd(c(subset(filter(LI_serum,Time == "PRE")[,i],!is.na(i),na.rm = TRUE), na.rm = TRUE)), 4)
  
  wilcox_LI[nrow, "Mean POST"] <-round(mean(subset(filter(LI_serum,Time == "POST")[,i],!is.na(i), na.rm = TRUE), 2, mean,  na.rm = TRUE), 4)
  
  wilcox_LI[nrow, "sd POST"] <- round(sd(c(subset(filter(LI_serum,Time == "POST")[,i],!is.na(i), na.rm = TRUE),na.rm = TRUE)), 4)
  
  wilcox_LI[nrow, "p.value"] <- round(p, 4) }


write.table(wilcox_LI, file = '/Users/student05/Documents/serum lipids/Tabellen/LI.pre.post.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten der Serumlipide zu den Zeitpunkten PRE und POST
In Arbeit, unterteilt in Gylcerophospholipide und Sphingolipide

```{r}
LI_serum.melt2 <- melt(LI_serum, id.vars = 'Time', measure.vars = c('PC', 'PCO', 'PE', 'PI','PEP', 'LPC'))
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, LI=variable)
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/SL.KD.pdf",width=9, height=10)
ggplot(LI_serum.melt2,aes(x=Time, y=Concentration, fill= LI)) +
  xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/ml]') + 
  geom_boxplot(width = .7, lwd=0.6)+ theme_classic()+
  scale_fill_manual(labels = c("Phosphatidylcholine", "PCO", "Phosphatidylethanolamine", "Phosphatidylinositol", "PE based plasmalogens", "Lysophosphatidylcholine"),
                    values = c("#980043", "#dd1c77", "#df65b0", "#c994c7", "#d4b9da", "#f1eef6")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c( "POST")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),axis.text=element_text(size=16))+
  theme(legend.position="top")
dev.off()



LI_serum.melt1 <- melt(LI_serum, id.vars = 'Time', measure.vars = c('SM','CER'))
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, LI=variable)
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/SL2.KD.pdf",width=8, height=10)
ggplot(LI_serum.melt1,aes(x=Time, y=Concentration, fill= LI)) +
  xlab ('Zeitpunkt') + ylab ('Konzentration [nmol/ml]') + 
  geom_boxplot(width = .3, lwd=0.8)+ theme_classic()+
  scale_fill_manual(labels = c("Sphingomyelin", "Ceramide"),
                    values = c("#fa9fb5", "#fde0dd")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c( "POST")))+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),axis.text=element_text(size=16))+
  theme(legend.position="top")+
  expand_limits(y=c(0, 2300))
dev.off()
```

Alle Serumlipide zusammen, Prozentuale Verteilung

```{r}

LI_serum.melt <- melt(LI_serum, id.vars = 'Time', measure.vars = c('PC', 'PCO','SM', 'PE', 'PI','PEP', 'LPC','CER'))

LI_serum.melt <- dplyr::rename(LI_serum.melt, LI=variable)
LI_serum.melt <- dplyr::rename(LI_serum.melt, Concentration=value)

pdf("/Users/student05/Documents/fertige Plots/Serumlipids.alltimes.pdf",width=9, height=10)
ggplot(LI_serum.melt,aes(x=LI, y=Concentration, fill= LI)) +
  xlab ('Serumlipid') + ylab ('Konzentration [nmol/ml]') + 
  scale_x_discrete(labels=c("PC" = "Phosphatidylcholine", "PCO" = "Phosphatidylcholine Plasmogen", "SM" = "Sphingomyelin", "PE" = "Phosphatidylethanolamine", "PI" = "Phosphatidylinositol", "PEP" = "Phosphatidylethanolamin Plasmalogen", "LPC" = "Lysophosphatidylcholine", "CER" ="Ceramid"))+
  geom_boxplot(width = .5, lwd=0.5) + theme_classic()+
  scale_fill_manual(labels = c("Phosphatidylcholine", "Phosphatidylcholine plasmogen", "Sphingomyelin", "Phosphatidylethanolamine", "Phosphatidylinositol", "PE based plasmalogens", "Lysophosphatidylcholine", "Ceramide"),
                    values = c("#df65b0", "#df65b0", "#fa9fb5", "#df65b0", "#df65b0", "#df65b0", "#df65b0", "#fa9fb5")) +
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(angle=25, hjust=1))+
  theme(legend.position="top")
dev.off()


LI_serum.sum.melt <- melt(LI_serum, id.vars = 'Time', measure.vars = c('Sum', 'Sum.Membrane','Sum.Storage', 'Sum.Lyso'))
LI_serum.sum.melt <- rename(LI_serum.sum.melt, LI=variable)
LI_serum.sum.melt <- rename(LI_serum.sum.melt, Concentration=value)

ggplot(LI_serum.sum.melt,aes(x=Time, y=Concentration, fill= LI)) +
  xlab ('Time Point') + ylab ('Concentration [nmol/ml]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("Summary total", "Summary Membrane", "Summary Storage", "Summary Lyso"),
                    values = c("tomato", "yellowgreen", "steelblue2", "orchid2")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(legend.position="top")


LI_serum.p.melt <- melt(LI_serum, id.vars = 'Time', measure.vars = c( 'P.Membrane','P.Storage', 'P.Lyso'))
LI_serum.p.melt <- rename(LI_serum.p.melt, LI=variable)
LI_serum.p.melt <- rename(LI_serum.p.melt, Concentration=value)

ggplot(LI_serum.p.melt,aes(x=Time, y=Concentration, fill= LI)) +
  xlab ('Time Point') + ylab ('Concentration [nmol/ml]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c( "Percentage Membrane", "Percentage Storage", "Percentage Lyso"),
                    values = c("yellowgreen", "steelblue2", "orchid2")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(legend.position="top")

```

Plotten der einzelnen Serumlipide, linked by Proband

```{r}
ggpaired(LI_serum, x='Time', y='Sum', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Timepoint') + ylab('Concentration Summary serum lipids [nmol/ml]') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='PC', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('PC serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='PCO', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('PCO serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='SM', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('SM serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='LPC', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Timepoint') + ylab('LPC serum lipids Concentration [nmol/ml]') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='PI', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('PI serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='PEP', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('PEP serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='LPC', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('LPC serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='CER', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Timepoint') + ylab('CER serum lipids Concentration[nmol/ml]') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='CER', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('CER serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")+
  geom_point(aes(color=Time), alpha=0.5) +
  geom_boxplot(outlier.size=4, outlier.colour='blue', alpha=0.5)

ggpaired(LI_serum, x='Time', y='HexCer', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Timepoint') + ylab('HexCer serum lipids Concentration [nmol/ml]') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='Sum.Membrane', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Membrane serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='Sum.Storage', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Storage serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='Sum.Lyso', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Lyso serum lipids [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='P.Membrane', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Membrane serum lipids [%]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='LPC.PC', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('LPC/PC serum lipid ratio') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='CER.SM', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Cer/SM serum lipid ratio') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='HexCer.CER', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('HexCer/Cer serum lipid ratio') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

ggpaired(LI_serum, x='Time', y='PC.PE', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('PC/PE serum lipid ratio') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

```

Korrelationen zwischen einzelnen Serumlipiden, linked by Probands

```{r}
LI_serum.melt1 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('PC', 'LPC'))
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, LI=variable)
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, Concentration=value)

LI_serum.melt1$Time <- factor(LI_serum.melt1$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt1, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)




LI_serum.melt2 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('Sum.Storage', 'Sum.Membrane'))
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, LI=variable)
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, Concentration=value)

LI_serum.melt2$Time <- factor(LI_serum.melt2$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt2, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)




LI_serum.melt2 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('Sum.Membrane', 'Sum.Lyso'))
LI_serum.melt2 <- rename(LI_serum.melt2, LI=variable)
LI_serum.melt2 <- rename(LI_serum.melt2, Concentration=value)

LI_serum.melt2$Time <- factor(LI_serum.melt2$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt2, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)




LI_serum.melt3 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('CER.SM', 'HexCer.CER'))
LI_serum.melt3<- rename(LI_serum.melt3, LI=variable)
LI_serum.melt3 <- rename(LI_serum.melt3, Concentration=value)
 
LI_serum.melt3$Time <- factor(LI_serum.melt2$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt3, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")



LI_serum.melt4 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('CER.SM', 'LPC'))
LI_serum.melt4<- rename(LI_serum.melt4, LI=variable)
LI_serum.melt4 <- rename(LI_serum.melt4, Concentration=value)
 
LI_serum.melt4$Time <- factor(LI_serum.melt4$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt4, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('PC and LPC concentration [nmol/ml]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")



LI_serum.melt5 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('PC.PE'))
LI_serum.melt5<- dplyr::rename(LI_serum.melt5, LI=variable)
LI_serum.melt5 <- dplyr::rename(LI_serum.melt5, Concentration=value)

LI_serum.melt5$Time <- factor(LI_serum.melt5$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt5, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'LI', short.panel.labs = FALSE) +
  xlab('Timepoint') + ylab('PC/PE ratio') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ LI, scales="free")+
  theme(legend.position="none")




LI_serum.melt6 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('P.Storage', 'HexCer'))
LI_serum.melt6<- rename(LI_serum.melt6, LI=variable)
LI_serum.melt6 <- rename(LI_serum.melt6, Concentration=value)
LI_serum.melt6$Time <- factor(LI_serum.melt6$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt6, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")+
  theme(legend.position="none")



LI_serum.melt6 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('LPC', 'PE'))

LI_serum.melt6<- rename(LI_serum.melt6, LI=variable)
LI_serum.melt6 <- rename(LI_serum.melt6, Concentration=value)
LI_serum.melt6$Time <- factor(LI_serum.melt6$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt6, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")+
  theme(legend.position="none")


LI_serum.1 <- read.table("/Users/student05/Documents/serum lipids/serum lipids zahlen.1.2.txt", sep = '\t', comment='',head=T)


LI_serum.melt6 <- melt(LI_serum.1, id.vars = c('Proband'), measure.vars = c('Sum', 'PE'))
LI_serum.melt6<- rename(LI_serum.melt6, LI=variable)
LI_serum.melt6 <- rename(LI_serum.melt6, Concentration=value)
LI_serum.melt6$Time <- factor(LI_serum.melt6$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt6, x='LI', y='Concentration', color = 'black', fill = 'LI', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', short.panel.labs = FALSE) +
  xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  theme(legend.position="none")

```

Korrelationen zwischen den Serumlipiden, von welchen auch die Verhältnisse betrachtet wurden

```{r}
LI_serum.melt6 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('LPC', 'PE'))
LI_serum.melt6<- rename(LI_serum.melt6, LI=variable)
LI_serum.melt6 <- rename(LI_serum.melt6, Concentration=value)

LI_serum.melt6$Time <- factor(LI_serum.melt6$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt6, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")+
  theme(legend.position="none")





LI_serum.melt7 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('SM', 'CER'))
LI_serum.melt7<- dplyr::rename(LI_serum.melt7, LI=variable)
LI_serum.melt7 <- dplyr::rename(LI_serum.melt7, Concentration=value)
 
LI_serum.melt7$Time <- factor(LI_serum.melt7$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt7, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")+
  theme(legend.position="none")




LI_serum.melt8 <- melt(LI_serum, id.vars = c('Time', 'Proband'), measure.vars = c('CER.SM'))
LI_serum.melt8 <- dplyr::rename(LI_serum.melt8, LI=variable)
LI_serum.melt8 <- dplyr::rename(LI_serum.melt8, Concentration=value)
 

ggplot(LI_serum.melt8,aes(x=Time, y=Concentration, fill= LI)) +
  xlab ('Time Point') + ylab ('Concentration [nmol/ml]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("CER/SM ratio"),
                    values = c("tomato")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(legend.position="top")

ggpaired(LI_serum.melt8, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'LI', short.panel.labs = FALSE) +
  xlab('Timepoint') + ylab('CER/SM ratio') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ LI, scales="free")+
  theme(legend.position="none")




LI_serum.melt9 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('LPC', 'PC'))
LI_serum.melt9<- dplyr::rename(LI_serum.melt9, LI=variable)
LI_serum.melt9 <- dplyr::rename(LI_serum.melt9, Concentration=value)

LI_serum.melt9$Time <- factor(LI_serum.melt9$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt9, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")+
  theme(legend.position="none")



LI_serum.melt11 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('PC', 'PE'))
LI_serum.melt11<- dplyr::rename(LI_serum.melt11, LI=variable)
LI_serum.melt11 <- dplyr::rename(LI_serum.melt11, Concentration=value)

LI_serum.melt11$Time <- factor(LI_serum.melt11$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt11, x='LI', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'Time', short.panel.labs = FALSE) +
  xlab('Storage and Membrane concentration [%]') + ylab('Concentration') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ Time, scales="free")+
  theme(legend.position="none")


pdf("/Users/student05/Documents/fertige Plots/PC.PE.Korr2.pdf",width=8, height=10)
ggscatter(LI_serum, x='PC', y='PE', add = 'reg.line', cor.coef.coord = c(950, 80), cor.coef.size = 8,conf.int = TRUE,
          cor.coef = TRUE,color = "grey59",fill = "lightgray", cor.method = 'spearman', xlab= 'Phosphatidylcholinkonzentrationen [nmol/ml]', ylab = 'Phosphatidylethanolaminkonzentrationen [nmol/ml]')+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text( hjust=1))+
  geom_point(color='grey52')+
  theme(legend.position="none")+
  geom_point(color='black', size=2.5)
dev.off()

cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$PC, method = "spearman", exact = F)

cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$PCO, method = "spearman", exact = F)

cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$SM, method = "spearman", exact = F)

cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$PI, method = "spearman", exact = F)

cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$PEP, method = "spearman", exact = F)

cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$LPC, method = "spearman", exact = F)

cor.test(subset(filter(LI_serum))$PE, subset(filter(LI_serum))$CER, method = "spearman", exact = F)

p.adjust(c(0.7706423 ,0.0001885,3.964e-07, 5.833e-16, 0.3947, 0.3421,0.007615 ), method = 'BH', n=7)

```

Plotten der Serumlipid-Verhältnisse

```{r}

LI_serum.melt10 <- melt(LI_serum, id.vars = c('Time', 'Proband'), measure.vars = c('LPC.PC'))
LI_serum.melt10 <- dplyr::rename(LI_serum.melt10, LI=variable)
LI_serum.melt10 <- dplyr::rename(LI_serum.melt10, Concentration=value)


ggplot(LI_serum.melt10,aes(x=Time, y=Concentration, fill= LI)) +
  xlab ('Time Point') + ylab ('Concentration [nmol/ml]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("LPC/PC ratio"),
                    values = c("steelblue")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(legend.position="top")

ggpaired(LI_serum.melt10, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'LI', short.panel.labs = FALSE) +
  xlab('Timepoint') + ylab('LPC/PC ratio') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ LI, scales="free")+
  theme(legend.position="none")


LI_serum.melt12 <- melt(LI_serum, id.vars = 'Time', measure.vars = c('PC.PE'))
LI_serum.melt12 <- dplyr::rename(LI_serum.melt12, LI=variable)
LI_serum.melt12 <- dplyr::rename(LI_serum.melt12, Concentration=value)


ggplot(LI_serum.melt12,aes(x=Time, y=Concentration, fill= LI)) +
  xlab ('Time Point') + ylab ('Concentration [nmol/ml]') + 
  geom_boxplot() + 
  scale_fill_manual(labels = c("PC/PE ratio"),
                    values = c("darkgreen")) +
  stat_compare_means(method = "wilcox.test", paired = TRUE, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
  theme(legend.position="top")



LI_serum.melt5 <- melt(LI_serum, id.vars = c('Time','Proband'), measure.vars = c('PC.PE'))
LI_serum.melt5<- dplyr::rename(LI_serum.melt5, LI=variable)
LI_serum.melt5 <- dplyr::rename(LI_serum.melt5, Concentration=value)
 
LI_serum.melt5$Time <- factor(LI_serum.melt5$Time, levels = c("PRE", "POST"))

ggpaired(LI_serum.melt5, x='Time', y='Concentration', color = 'black', fill = 'Time', palette = c('whitesmoke','whitesmoke'), line.color = 'grey60', line.size = 0.4, group = 'Proband', facet.by = 'LI', short.panel.labs = FALSE) +
  xlab('Timepoint') + ylab('PC/PE ratio') +
  geom_text(aes(label=Proband),hjust=0, vjust=0)+
  facet_grid(.~ LI, scales="free")+
  theme(legend.position="none")

```

6.2 Korrelationsanalysen zwischen Ceramid und Omega6-FA

Laden und filtern der Metadaten

```{r}

LI_CER6 <- read.table("/Users/student05/Documents/omegga6:cer.txt", sep = '\t', comment='',head=T)
 
LI_CER6$Time <- factor(LI_CER6$Time, levels =c("PRE", "POST"))

LI_CER6 <- subset(filter(LI_CER6, !Proband == "33MP"))

```

Plotten der Korrelationen

In Arbeit

```{r}
pdf("/Users/student05/Documents/fertige Plots/Ceramid.Linolsäure.pdf",width=8.5, height=10)
ggscatter(LI_CER6, x='CER', y='Linolsaeure_mol',color = 'Time',size = 2.5, palette = c('skyblue', 'orchid'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(8, 1000), cor.coef.size = 8, xlab= 'Ceramid Konzentrationen [nmol/ml]', ylab = 'Fäkale Linolsäurekonzentrationen [nmol/g]')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
dev.off()

ggscatter(LI_CER6, x='CER', y='Linolsaeure_mol', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'serum ceramide concentration [nmol/ml]', ylab = 'fecal linoleic fatty acid concentration [nmol/g]')+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")


ggscatter(LI_CER6, x='CER', y='Linolsaeure_i',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'serum CER concentration [nmol/ml]', ylab = 'fecal linoleic fatty acid concentration [nmol/g]')+
  facet_grid(.~ Time)+
  theme(strip.text.x = element_text(size = 8, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")
```

6.3 Erstellen einer Korrelationsmatrix zum testen von Korrelationen zwischen den Serumlipiden

Filtern für PRE und POST

```{r}
LI_serum_matrix_PRE <- subset(filter(LI_serum, Time == "PRE"))[ ,7:26]
LI_serum_matrix_POST <- subset(filter(LI_serum, Time == "POST"))[ ,7:26]

res.PRE <- cor(LI_serum_matrix_PRE)
res.POST <- cor(LI_serum_matrix_POST)

```

Spearman-Rangkorrelation und hinzufügen von Korrelationkoeffizient und p-value

```{r}
res2.PRE <- rcorr(as.matrix(LI_serum_matrix_PRE), type = "spearman")
res2.POST <- rcorr(as.matrix(LI_serum_matrix_POST), type = "spearman")


res2.PRE$r
res2.POST$r

LI_serum_PRE_CC <- as.matrix((res2.PRE$r))
LI_serum_POST_CC <- as.matrix(res2.POST$r)



res2$P

LI_serum_PRE_PV <- as.matrix(res2.PRE$P)
LI_serum_POST_PV <- as.matrix(res2.POST$P)
```

Erstellen einer flattenCorrMatrix für PRE und POST

```{r}

flattenCorrMatrix.PRE <- function(LI_serum_PRE_CC, LI_serum_PRE_PV) {
  ut <- upper.tri(LI_serum_PRE_CC)
  data.frame(
    row = rownames(LI_serum_PRE_CC)[row(LI_serum_PRE_CC)[ut]],
    column = rownames(LI_serum_PRE_CC)[col(LI_serum_PRE_CC)[ut]],
    cor  =(LI_serum_PRE_CC)[ut],
    p = LI_serum_PRE_PV[ut]
  )
}

flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P)



flattenCorrMatrix.POST <- function(LI_serum_POST_CC, LI_serum_POST_PV) {
  ut <- upper.tri(LI_serum_POST_CC)
  data.frame(
    row = rownames(LI_serum_POST_CC)[row(LI_serum_POST_CC)[ut]],
    column = rownames(LI_serum_POST_CC)[col(LI_serum_POST_CC)[ut]],
    cor  =(LI_serum_POST_CC)[ut],
    p = LI_serum_POST_PV[ut]
  )
}

flattenCorrMatrix.POST(res2.POST$r, res2.POST$P)

```

Dataframe erstellen

```{r}
LI_PRE_cor.p <- as.data.frame(flattenCorrMatrix.PRE(res2.PRE$r, res2.PRE$P))
LI_POST_cor.p <- as.data.frame(flattenCorrMatrix.POST(res2.POST$r, res2.POST$P))

colnames(LI_PRE_cor.p) <- c("LI", "LI", "correlation coefficient", "p-value")

colnames(LI_POST_cor.p) <- c("LI", "LI", "correlation coefficient", "p-value")

```

Corrplot erstellen zu den Zeiten PRE und POST

```{r}
corrplot(res.PRE, type = "upper", order = "hclust", 
         tl.col = "black", tl.srt = 45)

corrplot(res.POST, type = "upper", order = "hclust", 
         tl.col = "black", tl.srt = 45)


corrplot(res2.PRE$r, type="upper", order="hclust", 
         p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")

corrplot(res2.PRE$r, type="upper", order="hclust", 
         p.mat = res2.PRE$P, sig.level = 0.05, insig = "blank")
```

Scatterplots erstellen zu den Zeiten PRE und POST

```{r}
chart.Correlation(LI_serum_matrix_PRE, histogram=TRUE, pch=19)
chart.Correlation(SCFA_stool_matrix_POST, histogram = T, pch = 19)
```

6.4 Unterschiede der Serumlipidkonzentrationen zwischen Sterolkonvertierungstypen

Laden und filtern der Daten Lipidmetadaten s.o.
In high und low converter unterteilen

```{r}

lowconv <- filter(LI_serum, Proband == "05AP" | Proband == "33MP"
                  
                  | Proband == "38AR" | Proband == "40WA" | Proband == "41ML"
                  
                  | Proband == "47OT" | Proband == "49RJ" | Proband == "50DM")

lowconv['Phenotype'] = 'low converter'

highconv <- filter(LI_serum, Proband == "06WT" | Proband == "07RW"
                   
                   | Proband == "13BS" | Proband == "17SK" | Proband == "22WS"
                   
                   | Proband == "25FE" | Proband == "26FB" | Proband == "29MK"
                   
                   | Proband == "30HB" | Proband == "31KE" | Proband == "36ER"
                   
                   | Proband == "45GL" | Proband == "53BD" | Proband == "54SL"
                   
                   | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")

highconv['Phenotype'] = 'high converter'

highconv$Converter.Type <- NULL
lowconv$Converter.Type <- NULL

noconv <- filter(LI_serum, Proband == "28HM" | Proband == "32FG"
                 
                 | Proband == "34WF" | Proband == "35AD" | Proband == "37SD"
                 
                 | Proband == "39DA" | Proband == "66DG" | Proband == "70PL")

noconv['Phenotype'] = 'not classified'

noconv$Converter.Type <- NULL

convT <- data.frame()
convT <- bind_rows(lowconv, highconv, noconv)

convT_paired <- filter(convT, Proband == "05AP" | Proband == "06WT"
                       
                       | Proband == "07RW" | Proband == "13BS" | Proband == "17SK"
                       
                       | Proband == "22WS" | Proband == "25FE" | Proband == "26FB"
                       
                       | Proband == "28HM" | Proband == "29MK" | Proband == "30HB"
                       
                       | Proband == "31KE" | Proband == "32FG" | Proband == "36ER"
                       
                       | Proband == "37SD" | Proband == "38AR" | Proband == "40WA"
                       
                       | Proband == "41ML" | Proband == "45GL" | Proband == "47OT"
                       
                       | Proband == "50DM" | Proband == "53BD" | Proband == "54SL"
                       
                       | Proband == "57MT" | Proband == "69HL" | Proband == "74SA")

```

Plotten der Unterschiede der Serumlipidkonzentrationen zwischen den Sterolkonvertierungstypen

```{r}
LI_serum.melt <- melt(convT_paired, id.vars = c('Phenotype', 'Time'), measure.vars = c('PC', 'PCO', 'SM', 'PE', 'PI','PEP', 'LPC', 'CER', 'HexCer'))
LI_serum.melt <- subset(filter(LI_serum.melt, !Phenotype == "not classified"))
LI_serum.melt <- rename(LI_serum.melt, variable=LI)
LI_serum.melt <- rename(LI_serum.melt, Concentration=value)

 ggplot(LI_serum.melt,aes(x=Phenotype, y=value, fill= variable)) +
  xlab ('Converter type') + ylab ('Concentration [nmol/ml]') + 
  geom_boxplot()+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  facet_grid(.~Time)+
  theme(legend.position="top")


  
LI_serum.melt1 <- melt(convT_paired, id.vars = c('Phenotype', 'Time'), measure.vars = c('Sum', 'Sum.Membrane','Sum.Storage', 'Sum.Lyso'))
LI_serum.melt1 <- subset(filter(LI_serum.melt1, !Phenotype == "not classified"))
LI_serum.melt1 <- rename(LI_serum.melt1, variable=LI)
LI_serum.melt1 <- rename(LI_serum.melt1, Concentration=value)

  ggplot(LI_serum.melt1,aes(x=Phenotype, y=value, fill= variable)) +
  xlab ('Converter type') + ylab ('Concentration [nmol/ml]') + 
  geom_boxplot()+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  facet_grid(.~Time)+
  theme(legend.position="top")+
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))
```

Plotten der Unterschiede der Serumlipidverhältnisse zwischen den Sterolkonvertierungstypen

```{r}

LI.r1 <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('LPC.PC'))
  
  ggplot(filter(LI.r1, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("LPC/PC"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  
  
  
  
  LI.r2 <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('CER.SM'))
  
  ggplot(filter(LI.r2, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("CER/SM"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")

  
  
  
  
  LI_serum.melt2 <- melt(convT_paired, id.vars = c('Phenotype', 'Time'), measure.vars = c( 'PC.PE'))
  LI_serum.melt2 <- subset(filter(LI_serum.melt2, !Phenotype == "not classified"))
  LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, variable= LI)
  LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, Concentration=value)

  
  comparison_conv <- list(c("low converter", "high converter"))
  comparison_time <- list(c("PRE", "POST"))
  
  ggplot(LI_serum.melt2,aes(x=Phenotype, y=value, fill= variable)) +
    xlab ('Converter type') + ylab ('PC/PE ratio') + 
    geom_boxplot()+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=45, hjust=1))+
    facet_grid(.~Time)+
    theme(legend.position="none")+
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text( hjust=1))
  
  ggplot(LI_serum.melt2,aes(x=Time, y=value, fill= variable)) +
    xlab ('Converter type') + ylab ('PC/PE ratio') + 
    geom_boxplot()+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=0, hjust=1))+
    facet_grid(.~Phenotype)+
    theme(legend.position="none")+
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(hjust=1))
```

Wilcoxon-Test, mean und SD, Plotten der Unterschiede des PC/PE-Verhältnisses zwischen den Sterolkonvertierungstypen
In Arbeit

```{r}

  mean(subset(filter(convT_paired, Time == "PRE" & Phenotype == "high converter"))$PC.PE) 
  
  sd(subset(filter(convT_paired, Time == "PRE" & Phenotype == "high converter"))$PC.PE) 
  
  mean(subset(filter(convT_paired, Time == "POST" & Phenotype == "high converter"))$PC.PE) 
  
  sd(subset(filter(convT_paired, Time == "POST" & Phenotype == "high converter"))$PC.PE) 
  
  
  mean(subset(filter(convT_paired, Time == "PRE" & Phenotype == "low converter"))$PC.PE) 
  
  sd(subset(filter(convT_paired, Time == "PRE" & Phenotype == "low converter"))$PC.PE) 
  
  mean(subset(filter(convT_paired, Time == "POST" & Phenotype == "low converter"))$PC.PE) 
  
  sd(subset(filter(convT_paired, Time == "POST" & Phenotype == "low converter"))$PC.PE) 
  
  
  pairwise.wilcox.test(subset(filter(convT_paired, Time == "PRE"))$PC.PE, subset(filter(convT_paired, Time == "PRE"))$Phenotype, p.adjust.method = 'BH', paired = F)
  
  pairwise.wilcox.test(subset(filter(convT_paired, Time == "POST"))$PC.PE, subset(filter(convT_paired, Time == "POST"))$Phenotype, p.adjust.method = 'BH', paired = F)              
  
  pairwise.wilcox.test(subset(filter(convT_paired, Phenotype == "low converter"))$PC.PE, subset(filter(convT_paired, Phenotype == "low converter"))$Time, p.adjust.method = 'BH', paired = F)
  
  pairwise.wilcox.test(subset(filter(convT_paired, Phenotype == "high converter"))$PC.PE, subset(filter(convT_paired, Phenotype == "high converter"))$Time, p.adjust.method = 'BH', paired = F)
 
  
  
  
  
  LI.r2 <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PC.PE'))
  LI.r2 <- rename(  LI.r2, LI=variable)
  LI.r2 <- rename(  LI.r2, Concentration=value)

  pdf("/Users/student05/Documents/fertige Plots/converter.PC.PE.pdf",width=8, height=10)
  ggplot(filter(LI.r2, !Phenotype=="not classified"),aes(x=Phenotype, y=Concentration, fill= Phenotype)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Phosphatidylcholin/Phosphatidylethanolamin Verhältnis') + 
    scale_fill_manual(labels=c("high converter", "low converter"), values = c("seashell4", "seashell2"))+
    geom_boxplot(width = .7, lwd=0.6) + theme_classic() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("low converter")))+
    theme(strip.text.x = element_text(size = 18, colour = "black"))+
    theme(text = element_text(size=18, colour = "black"),
          axis.text.x = element_text(angle=0, hjust=1))+
    theme(legend.position="none")
  dev.off()
  
  ggplot(filter(LI.r2, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("PC/PE"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")

```

Plotten der Unterschiede einzelner Serumlipidkonzentrationen zwischen den Sterolkonvertierungstypen

```{r}
LI.PCO <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PCO'))
  LI.PCO <- rename(LI.PCO, FA=variable)
  LI.PCO <- rename(LI.PCO, Concentration=value)
  
  ggplot(filter(LI.PCO, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("PCO"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.PCO, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("PCO"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  
  
  
  
  LI.SM <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('SM'))
  LI.SM <- rename(LI.SM, FA=variable)
  LI.SM <- rename(LI.SM, Concentration=value)
  
  ggplot(filter(LI.SM, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("SM"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.SM, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("SM"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  
  
  

  LI.PE <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PE'))
  LI.PE <- rename(LI.PE, FA=variable)
  LI.PE <- rename(LI.PE, Concentration=value)
  
  ggplot(filter(LI.PE, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("PE"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.SM, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("SM"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  
  
 
  
  LI.PI <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PI'))
  LI.PI <- rename(LI.PI, FA=variable)
  LI.PI <- rename(LI.PI, Concentration=value)
  
  ggplot(filter(LI.PI, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("PI"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.PI, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("PI"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  

  
  
  LI.PEP <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('PEP'))
  LI.PEP <- rename(LI.PEP, FA=variable)
  LI.PEP <- rename(LI.PEP, Concentration=value)
  
  ggplot(filter(LI.PEP, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("PEP"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.PEP, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("PEP"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  
  

  
  LI.LPC <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('LPC'))
  LI.LPC <- rename(LI.LPC, FA=variable)
  LI.LPC <- rename(LI.LPC, Concentration=value)
  
  
  ggplot(filter(LI.LPC, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("LPC"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.LPC, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("LPC"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  

  
  
  LI.CER <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('CER'))
  
  ggplot(filter(LI.CER, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("CER"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.LPC, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("LPC"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  
  

  
  LI.HexCer <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('HexCer'))
  
  ggplot(filter(LI.HexCer, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("HEXCER"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.HexCer, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("HexCer"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  
  

  
 
  LI.Sum <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('Sum'))
  
  ggplot(filter(LI.Sum, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("SUM"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.HexCer, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("HexCer"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  

  

  
  
  LI.Sum.m <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('Sum.Membrane'))
  
  ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("SUM Membrane"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  

  
  
  

  LI.Sum.s <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('Sum.Storage'))
  
  ggplot(filter(LI.Sum.s, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("SUM Storage"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  

  
  LI.Sum.l <- melt(convT_paired, id.vars = c('Phenotype','Time'), measure.vars = c('Sum.Lyso'))
  
  ggplot(filter(LI.Sum.l, !Phenotype=="not classified"),aes(x=Phenotype, y=value, fill= variable)) +
    facet_grid(.~ Time) +
    xlab ('Converter type')+ ylab ('Concentration [nmol/ml] ') + 
    scale_fill_manual(labels=c("SUM Lyso"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = F, aes(labels = ..p.signif..), comparisons =list(c("high converter", "low converter")))+
    theme(text = element_text(size=13),
          axis.text.x = element_text(angle=60, hjust=1))+
    theme(legend.position="top")
  
  ggplot(filter(LI.Sum.m, !Phenotype=="not classified"),aes(x=Time, y=value, fill= variable)) +
    facet_grid(.~ Phenotype) +
    xlab ('Time Point')+ ylab ('Concentration [nmol/ml]') + 
    scale_fill_manual(labels=c("sum membrane"), values = c("steelblue2"))+
    geom_boxplot() +
    stat_compare_means(method = "wilcox.test", paired = T, aes(labels = ..p.signif..), comparisons =list(c("PRE", "POST")))+
    theme(legend.position="top")
  
```

6.4 alpha-Diversitätsanalysen mit Serumlipide

Laden, filtern und synchronisieren der Metadaten

```{r}
map_alphadiv <- read.table("/Users/student05/Downloads/means_alpha_div.txt", sep = '\t', comment='',head = TRUE, row.names = 1)

LI_serum <- read.table("/Users/student05/Documents/serum lipids zahlen.1-2.txt", sep = '\t', comment='',head=T)

LI_serum$Time <-factor(LI_serum$Time, levels = c("PRE", "POST"))

row.names(LI_serum) <- LI_serum$SampleID 

row.names(map_alphadiv)

common.ids.St <- intersect(rownames(LI_serum), rownames(map_alphadiv))

common.ids.St <- intersect(row.names(LI_serum), row.names(map_alphadiv))

LI_serum <- LI_serum[common.ids.St,]

map_alphadiv <- map_alphadiv[common.ids.St,]

LI_serum$Shannon <- map_alphadiv$Shannon

LI_serum$Simpson <- map_alphadiv$Simpson
```

Loop für Korrelationsanalyse zwischen Shannon-Index und Serumlipiden

```{r}
corr_colnames_LI <-colnames(LI_serum[,7:26])

corr_spearman_Shannon_LI <- data.frame()

for( i in unique(corr_colnames_LI)) {

  tmp <- filter(LI_serum, !is.na(i))

  x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))

  y = t(as.matrix(tmp$Shannon) )
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
  
  w = t(as.matrix(subset(filter(tmp, Time == "PRE"))$Shannon))
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
  
  s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Shannon))
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Shannon_LI)+1
 
  corr_spearman_Shannon_LI[nrow,"Div"] = "Shannon"
  
  corr_spearman_Shannon_LI[nrow, "column"] = i
  
  corr_spearman_Shannon_LI[nrow, "rho"] = rho
  
  corr_spearman_Shannon_LI[nrow, "p.value"] = p
  
  corr_spearman_Shannon_LI[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Shannon_LI[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Shannon_LI[nrow, "rho_POST"] = rho_POST
  
  corr_spearman_Shannon_LI[nrow, "p.value_POST"] = p_POST
  
}

corr_spearman_Shannon_LI$p.adjusted <- p.adjust(corr_spearman_Shannon_LI$p.value,method = "BH", n = 20)

corr_spearman_Shannon_LI$p.adjusted_PRE <-p.adjust(corr_spearman_Shannon_LI$p.value_PRE, method = "BH", n = 20)

corr_spearman_Shannon_LI$p.adjusted_POST <- p.adjust(corr_spearman_Shannon_LI$p.value_POST, method = "BH", n = 20)

corr_spearman_Shannon_LI$p.adjusted_FU <- p.adjust(corr_spearman_Shannon_LI$p.value_FU, method = "BH", n = 20)

write.table(corr_spearman_Shannon_LI, file = '/Users/student05/Documents/serum lipids/diversity/LI.Shannon.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

Plotten von Korrelationen zwischen Shannon-Index und Serumlipiden

alle Lipide

```{r}
LI_serum.melt <- melt(LI_serum, id.vars = c('Time','Shannon'), measure.vars = c( 'PC', 'PCO', 'SM', 'PE', 'PI','PEP', 'LPC', 'CER', 'HexCer'))
LI_serum.melt <- dplyr::rename(LI_serum.melt, LI=variable)
LI_serum.melt <- dplyr::rename(LI_serum.melt, Concentration=value)
LI_serum.melt.pr <- subset(filter(LI_serum.melt, !Time =='POST'))
LI_serum.melt.po <- subset(filter(LI_serum.melt, !Time =='PRE'))

ggscatter(LI_serum.melt.pr, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2', 'deeppink', 'brown4', 'darkorange1', 'blueviolet', 'aquamarine3'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c( 100, 7), xlab= 'serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
  facet_grid(.~ LI,scales = "free_x")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(LI_serum.melt, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2', 'deeppink', 'brown4', 'darkorange1', 'blueviolet', 'aquamarine3'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
  facet_grid(.~ LI,scales = "free_x")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(LI_serum.melt.po, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2', 'deeppink', 'brown4', 'darkorange1', 'blueviolet', 'aquamarine3'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE,cor.coef.coord = c(NULL, NULL),
 cor.method = 'spearman', xlab= 'serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
  facet_grid(.~ LI,scales = "free_x")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Summierten Lipidkonzentrationen

```{r}

LI_serum.melt1 <- melt(LI_serum, id.vars = c('Time','Shannon'), measure.vars = c( 'Sum', 'Sum.Membrane','Sum.Storage', 'Sum.Lyso'))
LI_serum.melt1 <- dplyr::rename(LI_serum.melt1, LI=variable)
LI_serum.melt1<- dplyr::rename(LI_serum.melt1, Concentration=value)
LI_serum.melt1.pr <- subset(filter(LI_serum.melt1, !Time =='POST'))
LI_serum.melt1.po <- subset(filter(LI_serum.melt1, !Time =='PRE'))

ggscatter(LI_serum.melt1.pr, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0, 7), xlab= 'sum serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
  facet_grid(.~ LI,scales = "free_x")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(LI_serum.melt1.po, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'serum lipid Concentration [nmol/ml]', ylab = 'Shannon-Index')+
  facet_grid(.~ LI,scales = "free_x")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Lipidverhältnisse

```{r}
LI_serum.melt2 <- melt(LI_serum, id.vars = c('Time','Shannon'), measure.vars = c( 'LPC.PC','CER.SM', 'PC.PE'))
LI_serum.melt2 <- dplyr::rename(LI_serum.melt2, LI=variable)
LI_serum.melt2<- dplyr::rename(LI_serum.melt2, Concentration=value)
LI_serum.melt2.pr <- subset(filter(LI_serum.melt2, !Time =='POST'))
LI_serum.melt2.po <- subset(filter(LI_serum.melt2, !Time =='PRE'))

ggscatter(LI_serum.melt2.pr, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0.03, 7), xlab= 'ratio serum lipids', ylab = 'Shannon-Index')+
  facet_grid(.~ LI,scales = "free_x")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(LI_serum.melt2.po, x='Concentration', y='Shannon',color = 'LI', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0.03, 7), xlab= 'ratio serum lipids', ylab = 'Shannon-Index')+
  facet_grid(.~ LI,scales = "free_x")+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(LI_serum, x='PC.PE', y='Shannon',color = 'Time', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(40, 7),cor.coef.size = 6, xlab= 'PC/PE serum lipid ratio', ylab = 'Shannon-Index')+
  facet_grid(.~ Time,scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")

ggscatter(LI_serum, x='PC.PE', y='Shannon', palette = c('tomato', 'yellowgreen', 'steelblue2', 'orchid2'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(40, 7),cor.coef.size = 6, xlab= 'PC/PE serum lipid ratio', ylab = 'Shannon-Index')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(angle=0, hjust=1))+
  theme(legend.position="none")
```

Loop für Korrelationsanalyse zwischen Simpson-Index und Serumlipiden

```{r}
corr_spearman_Simpson_LI <- data.frame()

for( i in unique(corr_colnames_LI)) {
 
  tmp <- filter(LI_serum, !is.na(i))

  x = as.matrix(as.data.frame(lapply(tmp[,i], as.numeric)))

  y = t(as.matrix(tmp$Simpson))

  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "PRE"))[,i],as.numeric)))
  
  w = t(as.matrix (subset(filter(tmp, Time == "PRE"))$Simpson))
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
 
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = as.matrix(as.data.frame(lapply(subset(filter(tmp, Time == "POST"))[,i],as.numeric)))
  
  s = t(as.matrix(subset(filter(tmp, Time == "POST"))$Simpson))
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Simpson_LI)+1
 
  corr_spearman_Simpson_LI[nrow,"Div"] = "Simpson"
  
  corr_spearman_Simpson_LI[nrow, "column"] = i
  
  corr_spearman_Simpson_LI[nrow, "rho"] = rho
  
  corr_spearman_Simpson_LI[nrow, "p.value"] = p
  
  corr_spearman_Simpson_LI[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Simpson_LI[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Simpson_LI[nrow, "rho_POST"] = rho_POST
  
  corr_spearman_Simpson_LI[nrow, "p.value_POST"] = p_POST

  
}

corr_spearman_Simpson_LI$p.adjusted <- p.adjust(corr_spearman_Simpson_LI$p.value,method = "BH", n = 20)

corr_spearman_Simpson_LI$p.adjusted_PRE <-p.adjust(corr_spearman_Simpson_LI$p.value_PRE, method = "BH", n = 20)

corr_spearman_Simpson_LI$p.adjusted_POST <- p.adjust(corr_spearman_Simpson_LI$p.value_POST, method = "BH", n = 20)

corr_spearman_Simpson_LI$p.adjusted_FU <- p.adjust(corr_spearman_Simpson_LI$p.value_FU, method = "BH", n = 20)


write.table(corr_spearman_Simpson_LI, file = '/Users/student05/Documents/serum lipids/diversity/LI.Simpson.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

```

-> gleicher Effekt wie bei Shannon-Index

6.5 Korrelationsanalysen zwischen dem relativen Vorkommen von Taxa und Serumlipiden

Laden und filtern der Metadaten

```{r}

map_KD <- read.table("/Users/student05/Documents/txt dateien r/Mappingfile_16SrRNA_BC22.txt", sep ='\t', comment='', head=T,row.names = 1)

relab <- read.table("/Users/student05/Documents/relative abundance/L6_metadata_taxa_strict_stool.txt", sep = '\t', comment='', head=T)

relab_PRE <- filter(relab, Time == "PRE")

relab_POST <- filter(relab, Time == "POST")

relab_FU <- filter(relab, Time == "FOLLOW-UP")

relab_means_PRE <- aggregate(relab_PRE[, 10:90], list(relab_PRE$Proband), mean)

relab_means_PRE['Time'] = 'PRE'

relab_means_PRE <- dplyr::rename(relab_means_PRE, Proband=Group.1)

relab_means_POST <- aggregate(relab_POST[, 10:90], list(relab_POST$Proband), mean)

relab_means_POST['Time'] = 'POST'

relab_means_POST <- dplyr::rename(relab_means_POST, Proband=Group.1)

relab_means_FU <- aggregate(relab_FU[, 10:90], list(relab_FU$Proband), mean)

relab_means_FU['Time'] = 'FOLLOW-UP'

relab_means_FU <- dplyr::rename(relab_means_FU, Proband=Group.1)

relab_means <- data_frame()

relab_means <- bind_rows(relab_means_PRE, relab_means_POST, relab_means_FU)

relab_means <- relab_means[, c(1, 83, 2:82)]

relab_means_melt <- melt(relab_means, id=c('Proband', 'Time'))

relab_means_melt <- dplyr::rename(relab_means_melt, Taxa=variable)

relab_means_melt <- dplyr::rename(relab_means_melt, Relative_Abundance=value)


relab_phylum <- subset(relab_means_melt, !grepl("g__|f__|o__|c__", relab_means_melt$Taxa))

relab_phylum <- subset(relab_phylum, !grepl("k__Archaea", relab_phylum$Taxa))

relab_phylum$Time <- factor(relab_phylum$Time, levels=c('PRE','POST','FOLLOW-UP'))

relab_phylum_spread <- spread(relab_phylum, Taxa, Relative_Abundance, sep = NULL)


relab_genus <- subset(relab_means_melt, grepl("g__", relab_means_melt$Taxa))

relab_genus <- subset(relab_genus, !grepl("k__Archaea", relab_genus$Taxa))

relab_genus$Time <- factor(relab_genus$Time, levels = c('PRE','POST','FOLLOW-UP'))

relab_genus_spread <- spread(relab_genus, Taxa, Relative_Abundance, sep = NULL)

```

Laden der Serumlipidmetadaten, Synchonisieren der Metadaten

```{r}

LI_serum <- read.table("/Users/student05/Documents/serum lipids zahlen.1-2.txt", sep = '\t', comment='',head=T)

LI_serum$Time <-factor(LI_serum$Time, levels = c("PRE", "POST"))

relab_phylum_ID <- relab_phylum_spread

relab_phylum_ID <- mutate(relab_phylum_ID, SampleID = paste(Proband, Time,sep="."))

row.names(relab_phylum_ID) <- relab_phylum_ID$SampleID

relab_genus_ID <- relab_genus_spread

relab_genus_ID <- mutate(relab_genus_ID, SampleID = paste(Proband, Time, sep ="."))

row.names(relab_genus_ID) <- relab_genus_ID$SampleID

LI_serum <- mutate(LI_serum, SampleID1 = paste(Proband, Time, sep = "."))

row.names(LI_serum) <- LI_serum$SampleID1

common.ids.relab <- intersect(rownames(LI_serum), rownames(relab_phylum_ID))

LI_serum <- LI_serum[common.ids.relab,]

relab_phylum_ID <- relab_phylum_ID[common.ids.relab,]

relab_genus_ID <- relab_genus_ID[common.ids.relab,]

```

Subsetten des Phylum-levels, log-Transformation und hinzufühen von Pseudocount 0.00001
Filtern nach Proben mit PRE und POST

```{r}

phylum_colnames <- colnames(relab_phylum_spread[, c(3:8)])

relab_phylum_ID1 <- relab_phylum_ID[,c(3:8)] + 0.00001

relab_phylum_ID_log <- log10(relab_phylum_ID_log)

phylum_LI <- cbind(relab_phylum_ID1, LI_serum[, c(1:27)])

phylum_LI$Proband

phylum_LI <- subset(filter(phylum_LI, !Proband == '31KE'))
phylum_LI <- subset(filter(phylum_LI, !Proband == '45GL'))
phylum_LI <- subset(filter(phylum_LI, !Proband == '34WF'))
phylum_LI <- subset(filter(phylum_LI, !Proband == '54SL'))
phylum_LI <- subset(filter(phylum_LI, !Proband == '74SA'))

phylum_LI$Time <- factor(phylum_LI$Time, levels = c("PRE", "POST"))

```

Loop und Plots Korrelation zwischen Phosphatidylcholin und phylum-level

In Arbeit

```{r}

corr_map_phylum_PC <- filter(phylum_LI, !is.na(PC))

corr_spearman_Phylum_PC <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_PC, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$PC
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$PC
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$PC
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_PC)+1

  corr_spearman_Phylum_PC[nrow,"FA"] <- "PC"
  
  corr_spearman_Phylum_PC[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_PC[nrow, "p.value"] = p
  
  corr_spearman_Phylum_PC[nrow, "rho"] = rho
  
  corr_spearman_Phylum_PC[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_PC[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_PC[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_PC[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_PC$p.adjusted <- p.adjust(corr_spearman_Phylum_PC$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_PC$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PC$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_PC$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PC$p.value_POST, method = "BH", n = 35)

write.table(corr_spearman_Phylum_PC, file = '/Users/student05/Documents/serum lipids/phylum/PC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Phosphatidylcholinkonzentrationen [nmol/ml]', ylab = 'log10 (Relatives Vorkommen p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


pdf("/Users/student05/Documents/fertige Plots/PC.Proteo.pdf",width=8, height=10)
ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('skyblue', 'orchid'),size = 2.5, add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(1000, -1.5), cor.coef.size = 8,xlab= 'Phosphatidylcholinkonzentrationen [nmol/ml]', ylab = 'Relatives Vorkommen p__Proteobacteria [%]')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  scale_y_log10(labels = percent_format())+
  theme(legend.position="none")
dev.off()

ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Proteobacteria', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(1000, -1.5), cor.coef.size = 5,xlab= 'PC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


ggscatter(phylum_LI, x='PC', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


```

Loop und Plots Korrelation zwischen Phosphatidylcholin-Plasmalogen und phylum-level

```{r}

corr_map_phylum_PCO <- filter(phylum_LI, !is.na(PCO))

corr_spearman_Phylum_PCO <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_PCO, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$PCO
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$PCO
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$PCO
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_PCO)+1

  corr_spearman_Phylum_PCO[nrow,"FA"] <- "PCO"
  
  corr_spearman_Phylum_PCO[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_PCO[nrow, "p.value"] = p
  
  corr_spearman_Phylum_PCO[nrow, "rho"] = rho
  
  corr_spearman_Phylum_PCO[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_PCO[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_PCO[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_PCO[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_PCO$p.adjusted <- p.adjust(corr_spearman_Phylum_PCO$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_PCO$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PCO$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_PCO$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PCO$p.value_POST, method = "BH", n = 35)

write.table(corr_spearman_Phylum_PCO, file = '/Users/student05/Documents/serum lipids/phylum/PCO.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PCO', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen Phosphatidylethanolamin und phylum-level

In Arbeit
```{r}

corr_map_phylum_PE <- filter(phylum_LI, !is.na(PE))

corr_spearman_Phylum_PE <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_PE, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$PE
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$PE
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$PE
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_PE)+1
  
  corr_spearman_Phylum_PE[nrow,"FA"] <- "PE"
  
  corr_spearman_Phylum_PE[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_PE[nrow, "p.value"] = p
  
  corr_spearman_Phylum_PE[nrow, "rho"] = rho
  
  corr_spearman_Phylum_PE[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_PE[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_PE[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_PE[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_PE$p.adjusted <- p.adjust(corr_spearman_Phylum_PE$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_PE$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PE$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_PE$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PE$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_PE, file = '/Users/student05/Documents/serum lipids/phylum/PE.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(10, -0.9), cor.coef.size = 5, xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(10, -0.9), cor.coef.size = 5, xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

pdf("/Users/student05/Documents/fertige Plots/PE.proteo.pdf",width=8.5, height=10)
ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('skyblue', 'orchid'), size = 2.5, add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(15, -1.5), cor.coef.size = 8,xlab= 'Phosphatidylethanolaminkonzentrationen [nmol/ml]', ylab = 'Relatives Vorkommen p__Proteobacteria [%]')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  scale_y_log10(labels = percent_format())+
  theme(legend.position="none")
dev.off()

ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Proteobacteria', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]',cor.coef.coord = c(20, -1.4), cor.coef.size = 5, ylab = 'log10 (Relative Abundance p__Proteobacteria)')+theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PE', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen Sphingomyelin und phylum-level

```{r}

corr_map_phylum_SM <- filter(phylum_LI, !is.na(SM))

corr_spearman_Phylum_SM <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_SM, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$SM
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$SM
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$SM
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_SM)+1
  
  corr_spearman_Phylum_SM[nrow,"FA"] <- "SM"
  
  corr_spearman_Phylum_SM[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_SM[nrow, "p.value"] = p
  
  corr_spearman_Phylum_SM[nrow, "rho"] = rho
  
  corr_spearman_Phylum_SM[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_SM[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_SM[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_SM[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_SM$p.adjusted <- p.adjust(corr_spearman_Phylum_SM$p.value, method = "BH", n = 35)

corr_spearman_Phylum_SM$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_SM$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_SM$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_SM$p.value_POST, method = "BH", n = 35)

write.table(corr_spearman_Phylum_SM, file = '/Users/student05/Documents/serum lipids/phylum/SM.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipids concentration [nmol/ml]',cor.coef.coord = c(200, -0.9), cor.coef.size = 4, ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text(angle=45, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipids concentration [nmol/ml]',cor.coef.coord = c(200, -0.9), cor.coef.size = 4, ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='SM', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen Phosphatidylinositol und phylum-level

```{r}

corr_map_phylum_PI <- filter(phylum_LI, !is.na(PI))

corr_spearman_Phylum_PI <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_PI, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$PI
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$PI
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$PI
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_PI)+1
  
  corr_spearman_Phylum_PI[nrow,"FA"] <- "PI"
  
  corr_spearman_Phylum_PI[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_PI[nrow, "p.value"] = p
  
  corr_spearman_Phylum_PI[nrow, "rho"] = rho
  
  corr_spearman_Phylum_PI[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_PI[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_PI[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_PI[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_PI$p.adjusted <- p.adjust(corr_spearman_Phylum_PI$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_PI$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PI$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_PI$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PI$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_PI, file = '/Users/student05/Documents/serum lipids/phylum/PI.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PI', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen Phosphatidylethanolamin-Plasmalogen und phylum-level

```{r}

corr_map_phylum_PEP <- filter(phylum_LI, !is.na(PEP))

corr_spearman_Phylum_PEP <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_PEP, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$PEP
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$PEP
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$PEP
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_PEP)+1
 
  corr_spearman_Phylum_PEP[nrow,"FA"] <- "PEP"
  
  corr_spearman_Phylum_PEP[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_PEP[nrow, "p.value"] = p
  
  corr_spearman_Phylum_PEP[nrow, "rho"] = rho
  
  corr_spearman_Phylum_PEP[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_PEP[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_PEP[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_PEP[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_PEP$p.adjusted <- p.adjust(corr_spearman_Phylum_PEP$p.value, method = "BH", n = 35)

corr_spearman_Phylum_PEP$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PEP$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_PEP$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PEP$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_PEP, file = '/Users/student05/Documents/serum lipids/phylum/PEP.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PEP', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen Lysophosphatidylcholin und phylum-level

```{r}

corr_map_phylum_LPC <- filter(phylum_LI, !is.na(LPC))

corr_spearman_Phylum_LPC <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_LPC, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$LPC
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$LPC
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$LPC
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_LPC)+1
  
  corr_spearman_Phylum_LPC[nrow,"FA"] <- "LPC"
  
  corr_spearman_Phylum_LPC[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_LPC[nrow, "p.value"] = p
  
  corr_spearman_Phylum_LPC[nrow, "rho"] = rho
  
  corr_spearman_Phylum_LPC[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_LPC[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_LPC[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_LPC[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_LPC$p.adjusted <- p.adjust(corr_spearman_Phylum_LPC$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_LPC$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_LPC$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_LPC$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_LPC$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_LPC, file = '/Users/student05/Documents/serum lipids/phylum/LPC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen Ceramid und phylum-level

```{r}

corr_map_phylum_CER <- filter(phylum_LI, !is.na(CER))

corr_spearman_Phylum_CER <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_CER, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$CER
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$CER
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$CER
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_CER)+1
 
  corr_spearman_Phylum_CER[nrow,"FA"] <- "CER"
  
  corr_spearman_Phylum_CER[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_CER[nrow, "p.value"] = p
  
  corr_spearman_Phylum_CER[nrow, "rho"] = rho
  
  corr_spearman_Phylum_CER[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_CER[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_CER[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_CER[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_CER$p.adjusted <- p.adjust(corr_spearman_Phylum_CER$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_CER$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_CER$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_CER$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_CER$p.value_POST, method = "BH", n = 35)

write.table(corr_spearman_Phylum_CER, file = '/Users/student05/Documents/serum lipids/phylum/CER.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Cer serum lipids concentration [nmol/ml]', cor.coef.coord = c(8, -0.9), cor.coef.size = 6,ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Bacteroidetes', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Cer serum lipids concentration [nmol/ml]', cor.coef.coord = c(8, -0.9), cor.coef.size = 6,ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'Cer serum lipids concentration [nmol/ml]',cor.coef.coord = c(8, -0.9), cor.coef.size = 6, ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```
Loop und Plots Korrelation zwischen Hexosylceramid und phylum-level

```{r}

corr_map_phylum_HexCer <- filter(phylum_LI, !is.na(HexCer))

corr_spearman_Phylum_HexCer <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_HexCer, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$HexCer
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$HexCer
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$HexCer
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value

  nrow = nrow(corr_spearman_Phylum_HexCer)+1
 
  corr_spearman_Phylum_HexCer[nrow,"FA"] <- "HexCer"
  
  corr_spearman_Phylum_HexCer[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_HexCer[nrow, "p.value"] = p
  
  corr_spearman_Phylum_HexCer[nrow, "rho"] = rho
  
  corr_spearman_Phylum_HexCer[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_HexCer[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_HexCer[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_HexCer[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_HexCer$p.adjusted <- p.adjust(corr_spearman_Phylum_HexCer$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_HexCer$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_HexCer$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_HexCer$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_HexCer$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_HexCer, file = '/Users/student05/Documents/serum lipids/phylum/HexCer.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, label = 'Proband',
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundancep__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen summierten Serumlipiden und phylum-level

```{r}

corr_map_phylum_Sum <- filter(phylum_LI, !is.na(Sum))

corr_spearman_Phylum_Sum <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_Sum, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Sum
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Sum
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Sum
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_Sum)+1
 
  corr_spearman_Phylum_Sum[nrow,"FA"] <- "Sum"
  
  corr_spearman_Phylum_Sum[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Sum[nrow, "p.value"] = p
  
  corr_spearman_Phylum_Sum[nrow, "rho"] = rho
  
  corr_spearman_Phylum_Sum[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Sum[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Sum[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Sum[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Sum$p.adjusted <- p.adjust(corr_spearman_Phylum_Sum$p.value, method = "BH", n = 35)

corr_spearman_Phylum_Sum$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Sum$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_Sum$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Sum$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_Sum, file = '/Users/student05/Documents/serum lipids/phylum/Sum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen summierten Membranlipiden und phylum-level

```{r}

corr_map_phylum_Sum.Membrane <- filter(phylum_LI, !is.na(Sum.Membrane))

corr_spearman_Phylum_Sum.Membrane <- data.frame()

for( i in phylum_colnames) {
  
  tmp <- filter(corr_map_phylum_Sum.Membrane, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$Sum.Membrane
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Sum.Membrane
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Sum.Membrane
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_Sum.Membrane)+1
  
  corr_spearman_Phylum_Sum.Membrane[nrow,"FA"] <- "Sum.Membrane"
  
  corr_spearman_Phylum_Sum.Membrane[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Sum.Membrane[nrow, "p.value"] = p
  
  corr_spearman_Phylum_Sum.Membrane[nrow, "rho"] = rho
  
  corr_spearman_Phylum_Sum.Membrane[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Sum.Membrane[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Sum.Membrane[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Sum.Membrane[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Sum.Membrane$p.adjusted <- p.adjust(corr_spearman_Phylum_Sum.Membrane$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_Sum.Membrane$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Sum.Membrane$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_Sum.Membrane$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Sum.Membrane$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_Sum.Membrane, file = '/Users/student05/Documents/serum lipids/phylum/Sum.Membrane.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'Summarized membrane serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Membrane', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und Plots Korrelation zwischen summierten Storagelipiden und phylum-level

```{r}

corr_map_phylum_Sum.Storage <- filter(phylum_LI, !is.na(Sum.Storage))

corr_spearman_Phylum_Sum.Storage <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_Sum.Storage, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Sum.Storage
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
  
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Sum.Storage
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Sum.Storage
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_Sum.Storage)+1
  
  corr_spearman_Phylum_Sum.Storage[nrow,"FA"] <- "Sum.Storage"
  
  corr_spearman_Phylum_Sum.Storage[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Sum.Storage[nrow, "p.value"] = p
  
  corr_spearman_Phylum_Sum.Storage[nrow, "rho"] = rho
  
  corr_spearman_Phylum_Sum.Storage[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Sum.Storage[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Sum.Storage[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Sum.Storage[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Sum.Storage$p.adjusted <- p.adjust(corr_spearman_Phylum_Sum.Storage$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_Sum.Storage$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Sum.Storage$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_Sum.Storage$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Sum.Storage$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_Sum.Storage, file = '/Users/student05/Documents/serum lipids/phylum/Sum.Storage.txt', sep ="\t", col.names = TRUE,row.names = FALSE)


ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'summarized storage serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'summarized storage serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, or.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Storage', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")


```

Loop und Plots Korrelation zwischen summierten Lysolipiden und phylum-level

```{r}
corr_map_phylum_Sum.Lyso <- filter(phylum_LI, !is.na(Sum.Lyso))

corr_spearman_Phylum_Sum.Lyso <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_Sum.Lyso, !is.na(i))
  
  y = tmp[,i]
  
  x = tmp$Sum.Lyso
  
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
  
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$Sum.Lyso
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$Sum.Lyso
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_Sum.Lyso)+1
  
  corr_spearman_Phylum_Sum.Lyso[nrow,"FA"] <- "Sum.Lyso"
  
  corr_spearman_Phylum_Sum.Lyso[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_Sum.Lyso[nrow, "p.value"] = p
  
  corr_spearman_Phylum_Sum.Lyso[nrow, "rho"] = rho
  
  corr_spearman_Phylum_Sum.Lyso[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_Sum.Lyso[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_Sum.Lyso[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_Sum.Lyso[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_Sum.Lyso$p.adjusted <- p.adjust(corr_spearman_Phylum_Sum.Lyso$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_Sum.Lyso$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_Sum.Lyso$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_Sum.Lyso$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_Sum.Lyso$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_Sum.Lyso, file = '/Users/student05/Documents/serum lipids/phylum/Sum.Lyso.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'summarized lyso serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'summarized lyso serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='Sum.Lyso', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```
Loop und PLots Korrelation zwischen LPC/PC-Verhältnis und phylum-level

```{r}

corr_map_phylum_LPC.PC <- filter(phylum_LI, !is.na(LPC.PC))

corr_spearman_Phylum_LPC.PC <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_LPC.PC, !is.na(i))

  y = tmp[,i]
  
  x = tmp$LPC.PC
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$LPC.PC
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$LPC.PC
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_LPC.PC)+1
 
  corr_spearman_Phylum_LPC.PC[nrow,"FA"] <- "LPC.PC"
  
  corr_spearman_Phylum_LPC.PC[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_LPC.PC[nrow, "p.value"] = p
  
  corr_spearman_Phylum_LPC.PC[nrow, "rho"] = rho
  
  corr_spearman_Phylum_LPC.PC[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_LPC.PC[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_LPC.PC[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_LPC.PC[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_LPC.PC$p.adjusted <- p.adjust(corr_spearman_Phylum_LPC.PC$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_LPC.PC$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_LPC.PC$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_LPC.PC$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_LPC.PC$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_LPC.PC, file = '/Users/student05/Documents/serum lipids/phylum/LPC.PC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipids ratio', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipids ratio', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(0.04, -0.75),xlab= 'LPC/PC serum lipids ratio', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='LPC.PC', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und PLots Korrelation zwischen CER/SM-Verhältnis und phylum-level

```{r}

corr_map_phylum_CER.SM <- filter(phylum_LI, !is.na(CER.SM))

corr_spearman_Phylum_CER.SM <- data.frame()

for( i in phylum_colnames) {
 
  tmp <- filter(corr_map_phylum_CER.SM, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$CER.SM
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")

  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value

  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$CER.SM
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value

  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$CER.SM
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
  
  nrow = nrow(corr_spearman_Phylum_CER.SM)+1
  
  corr_spearman_Phylum_CER.SM[nrow,"FA"] <- "CER.SM"
  
  corr_spearman_Phylum_CER.SM[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_CER.SM[nrow, "p.value"] = p
  
  corr_spearman_Phylum_CER.SM[nrow, "rho"] = rho
  
  corr_spearman_Phylum_CER.SM[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_CER.SM[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_CER.SM[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_CER.SM[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_CER.SM$p.adjusted <- p.adjust(corr_spearman_Phylum_CER.SM$p.value, method = "BH", n = 35)

corr_spearman_Phylum_CER.SM$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_CER.SM$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_CER.SM$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_CER.SM$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_CER.SM, file = '/Users/student05/Documents/serum lipids/phylum/CER.SM.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0.02, -0.9), xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE,  cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(0.03, -0.7),xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='CER.SM', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und PLots Korrelation zwischen HexCER/CER-Verhältnis und phylum-level

```{r}
corr_map_phylum_HexCer.CER <- filter(phylum_LI, !is.na(HexCer.CER))

corr_spearman_Phylum_HexCer.CER <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_HexCer.CER, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$HexCer.CER
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$HexCer.CER
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
 
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$HexCer.CER
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_HexCer.CER)+1
  
  corr_spearman_Phylum_HexCer.CER[nrow,"FA"] <- "HexCer.CER"
  
  corr_spearman_Phylum_HexCer.CER[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_HexCer.CER[nrow, "p.value"] = p
  
  corr_spearman_Phylum_HexCer.CER[nrow, "rho"] = rho
  
  corr_spearman_Phylum_HexCer.CER[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_HexCer.CER[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_HexCer.CER[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_HexCer.CER[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_HexCer.CER$p.adjusted <- p.adjust(corr_spearman_Phylum_HexCer.CER$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_HexCer.CER$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_HexCer.CER$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_HexCer.CER$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_HexCer.CER$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_HexCer.CER, file = '/Users/student05/Documents/serum lipids/phylum/HexCer.CER.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE,cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='HexCer.CER', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Loop und PLots Korrelation zwischen PC/PE-Verhältnis und phylum-level
In Arbeit

```{r}

corr_map_phylum_PC.PE <- filter(phylum_LI, !is.na(PC.PE))

corr_spearman_Phylum_PC.PE <- data.frame()

for( i in phylum_colnames) {

  tmp <- filter(corr_map_phylum_PC.PE, !is.na(i))
 
  y = tmp[,i]
  
  x = tmp$PC.PE
 
  tmp_corr_spearman <- cor.test(x, y, method="spearman")
 
  rho = tmp_corr_spearman$estimate
  
  p = tmp_corr_spearman$p.value
 
  z = subset(filter(tmp, Time == "PRE"))[,i]
  
  w = subset(filter(tmp, Time == "PRE"))$PC.PE
  
  tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
  
  rho_PRE = tmp_corr_spearman_PRE$estimate
  
  p_PRE = tmp_corr_spearman_PRE$p.value
  
  r = subset(filter(tmp, Time == "POST"))[,i]
  
  s = subset(filter(tmp, Time == "POST"))$PC.PE
  
  tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
  
  rho_POST = tmp_corr_spearman_POST$estimate
  
  p_POST = tmp_corr_spearman_POST$p.value
 
  nrow = nrow(corr_spearman_Phylum_PC.PE)+1
 
  corr_spearman_Phylum_PC.PE[nrow,"FA"] <- "PC.PE"
  
  corr_spearman_Phylum_PC.PE[nrow, "Phylum"] = i
  
  corr_spearman_Phylum_PC.PE[nrow, "p.value"] = p
  
  corr_spearman_Phylum_PC.PE[nrow, "rho"] = rho
  
  corr_spearman_Phylum_PC.PE[nrow, "p.value_PRE"] = p_PRE
  
  corr_spearman_Phylum_PC.PE[nrow, "rho_PRE"] = rho_PRE
  
  corr_spearman_Phylum_PC.PE[nrow, "p.value_POST"] = p_POST
  
  corr_spearman_Phylum_PC.PE[nrow, "rho_POST"] = rho_POST
  
}

corr_spearman_Phylum_PC.PE$p.adjusted <- p.adjust(corr_spearman_Phylum_PC.PE$p.value, method = "BH", n = 35) 

corr_spearman_Phylum_PC.PE$p.adjusted_PRE <- p.adjust(corr_spearman_Phylum_PC.PE$p.value_PRE, method = "BH", n = 35)

corr_spearman_Phylum_PC.PE$p.adjusted_POST <- p.adjust(corr_spearman_Phylum_PC.PE$p.value_POST, method = "BH", n = 35)


write.table(corr_spearman_Phylum_PC.PE, file = '/Users/student05/Documents/serum lipids/phylum/PC.PE.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Bacteroidetes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipids ratio',cor.coef.coord = c(30, -1.5), cor.coef.size = 6, ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

pdf("/Users/student05/Documents/fertige Plots/PC.PE.Proteo.pdf",width=8, height=10)
ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('skyblue', 'orchid'), size = 2.5, add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(30, -1.7 ), cor.coef.size = 7,xlab= 'PC/PE Verhältnis', ylab = 'Relatives Vorkommen p__Proteobacteria [%]')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 18, colour = "black"))+
  theme(text = element_text(size=18),
        axis.text.x = element_text(hjust=1))+
  scale_y_log10(labels = percent_format())+
  theme(legend.position="none")
dev.off()

ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipids ratio',cor.coef.coord = c(30, -1.4), cor.coef.size = 6, ylab = 'log10 (Relative Abundance p__Proteobacteria)')+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
  theme(text = element_text(size=15),
        axis.text.x = element_text( hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Verrucomicrobia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Verrucomicrobia)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Tenericutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Tenericutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER/SM serum lipids ratio', ylab = 'log10 (Relative Abundance p__Firmicutes)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Actinobacteria',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
          cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipids concentration [nmol/ml]', ylab = 'log10 (Relative Abundance p__Actinobacteria)')+
  facet_grid(.~ Time, scales = "free_x")+
  theme(strip.text.x = element_text(size = 6, colour = "black"))+
  theme(text = element_text(size=13),
        axis.text.x = element_text(angle=60, hjust=1))+
  theme(legend.position="none")

```

Korrelationsanalysen zwischen Genus-level und Serumlipiden

Subsetten des Genus-level, log-Transformation, hinzufügen von Pseudocount 0.0001
Filtern nach PRE und POST Proben

```{r}

genus_colnames <- colnames(relab_genus_spread[, c(3:31)])

relab_genus_ID1 <- relab_genus_ID[,c(3:31)] + 0.00001

relab_genus_ID_log <- log10(relab_genus_ID_log)

genus_LI <- cbind(relab_genus_ID1, LI_serum)

 genus_LI <- subset(filter(genus_LI, !Proband == '31KE'))
 genus_LI <- subset(filter(genus_LI, !Proband == '45GL'))
 genus_LI <- subset(filter(genus_LI, !Proband == '34WF'))
 genus_LI <- subset(filter(genus_LI, !Proband == '54SL'))
 genus_LI <- subset(filter(genus_LI, !Proband == '74SA'))
 
genus_LI$Time <- factor(genus_LI$Time, levels = c("PRE", "POST"))
 
```

Loop und Plots Korrelation zwischen Phosphatidylcholin und genus-level

```{r}
corr_map_genus_PC <- filter(genus_LI, !is.na(PC))
 
 corr_spearman_genus_PC <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_PC, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$PC
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$PC
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$PC
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_PC)+1
   
   corr_spearman_genus_PC[nrow,"FA"] = "PC"
   
   corr_spearman_genus_PC[nrow, "Genus"] = i
   
   corr_spearman_genus_PC[nrow, "p.value"] = p
   
   corr_spearman_genus_PC[nrow, "rho"] = rho
   
   corr_spearman_genus_PC[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_PC[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_PC[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_PC[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_PC$p.adjusted <- p.adjust(corr_spearman_genus_PC$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_PC$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PC$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_PC$p.adjusted_POST <- p.adjust(corr_spearman_genus_PC$p.value_POST, method = "BH", n = 35)
 
 write.table( corr_spearman_genus_PC, file = '/Users/student05/Documents/serum lipids/genus/PC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 
 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', cor.coef.coord = c(800, -1.6), cor.coef.size = 6,ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 15, colour = "black"))+
   theme(text = element_text(size=15),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', cor.coef.coord = c(800, -1.6), cor.coef.size = 6,ylab = 'log10 (Relative Abundance g__Coprococcus')+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
   theme(text = element_text(size=15),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]',cor.coef.coord = c(800, -2), cor.coef.size = 6, ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 15, colour = "black"))+
   theme(text = element_text(size=15),
         axis.text.x = element_text(angle=0, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]',cor.coef.coord = c(800, -2), cor.coef.size = 6, ylab = 'log10 (Relative Abundance g__Sutterella')+
  theme(strip.text.x = element_text(size = 15, colour = "black"))+
   theme(text = element_text(size=15),
         axis.text.x = element_text(angle=0, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen Phosphatidylcholin-Plasmalogen und genus-level

```{r}
corr_map_genus_PCO <- filter(genus_LI, !is.na(PCO))
 
 corr_spearman_genus_PCO <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_PCO, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$PCO
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$PCO
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$PCO
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_PCO)+1
   
   corr_spearman_genus_PCO[nrow,"FA"] = "PCO"
   
   corr_spearman_genus_PCO[nrow, "Genus"] = i
   
   corr_spearman_genus_PCO[nrow, "p.value"] = p
   
   corr_spearman_genus_PCO[nrow, "rho"] = rho
   
   corr_spearman_genus_PCO[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_PCO[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_PCO[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_PCO[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_PCO$p.adjusted <- p.adjust(corr_spearman_genus_PCO$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_PCO$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PCO$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_PCO$p.adjusted_POST <- p.adjust(corr_spearman_genus_PCO$p.value_POST, method = "BH", n = 35)
 

 write.table( corr_spearman_genus_PCO, file = '/Users/student05/Documents/serum lipids/genus/PCO.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PCO', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PCO serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen Sphingomyelin und genus-level

```{r}
 corr_map_genus_SM <- filter(genus_LI, !is.na(SM))
 
 corr_spearman_genus_SM <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_SM, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$SM
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$SM
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$SM
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_SM)+1
   
   corr_spearman_genus_SM[nrow,"FA"] = "SM"
   
   corr_spearman_genus_SM[nrow, "Genus"] = i
   
   corr_spearman_genus_SM[nrow, "p.value"] = p
   
   corr_spearman_genus_SM[nrow, "rho"] = rho
   
   corr_spearman_genus_SM[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_SM[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_SM[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_SM[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_SM$p.adjusted <- p.adjust(corr_spearman_genus_SM$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_SM$p.adjusted_PRE <- p.adjust(corr_spearman_genus_SM$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_SM$p.adjusted_POST <- p.adjust(corr_spearman_genus_SM$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_SM, file = '/Users/student05/Documents/serum lipids/genus/SM.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]',cor.coef.size = 6,cor.coef.coord = c(200, -1.5), ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 15, colour = "black"))+
   theme(text = element_text(size=15),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]',cor.coef.size = 6,cor.coef.coord = c(200, -1.5), ylab = 'log10 (Relative Abundance g__Coprococcus')+
   theme(strip.text.x = element_text(size = 15, colour = "black"))+
   theme(text = element_text(size=15),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
```

Loop und Plots Korrelation zwischen Phosphatidylethanolamin und genus-level

```{r}
corr_map_genus_PE <- filter(genus_LI, !is.na(PE))
 
 corr_spearman_genus_PE <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_PE, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$PE
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$PE
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$PE
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_PE)+1
   
   corr_spearman_genus_PE[nrow,"FA"] = "PE"
   
   corr_spearman_genus_PE[nrow, "Genus"] = i
   
   corr_spearman_genus_PE[nrow, "p.value"] = p
   
   corr_spearman_genus_PE[nrow, "rho"] = rho
   
   corr_spearman_genus_PE[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_PE[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_PE[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_PE[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_PE$p.adjusted <- p.adjust(corr_spearman_genus_PE$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_PE$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PE$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_PE$p.adjusted_POST <- p.adjust(corr_spearman_genus_PE$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_PE, file = '/Users/student05/Documents/serum lipids/genus/PE.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen Phosphatidylinositol und genus-level

```{r}
corr_map_genus_PI <- filter(genus_LI, !is.na(PI))
 
 corr_spearman_genus_PI <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_PI, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$PI
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$PI
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$PI
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_PI)+1
   
   corr_spearman_genus_PI[nrow,"FA"] = "PI"
   
   corr_spearman_genus_PI[nrow, "Genus"] = i
   
   corr_spearman_genus_PI[nrow, "p.value"] = p
   
   corr_spearman_genus_PI[nrow, "rho"] = rho
   
   corr_spearman_genus_PI[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_PI[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_PI[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_PI[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_PI$p.adjusted <- p.adjust(corr_spearman_genus_PI$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_PI$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PI$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_PI$p.adjusted_POST <- p.adjust(corr_spearman_genus_PI$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_PI, file = '/Users/student05/Documents/serum lipids/genus/PI.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PI', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PI serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen Phosphatidylethanolamin-Plasmalogen und genus-level

```{r}

 corr_map_genus_PEP <- filter(genus_LI, !is.na(PEP))
 
 corr_spearman_genus_PEP <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_PEP, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$PEP
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$PEP
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$PEP
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_PEP)+1
   
   corr_spearman_genus_PEP[nrow,"FA"] = "PEP"
   
   corr_spearman_genus_PEP[nrow, "Genus"] = i
   
   corr_spearman_genus_PEP[nrow, "p.value"] = p
   
   corr_spearman_genus_PEP[nrow, "rho"] = rho
   
   corr_spearman_genus_PEP[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_PEP[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_PEP[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_PEP[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_PEP$p.adjusted <- p.adjust(corr_spearman_genus_PEP$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_PEP$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PEP$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_PEP$p.adjusted_POST <- p.adjust(corr_spearman_genus_PEP$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_PEP, file = '/Users/student05/Documents/serum lipids/genus/PEP.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PEP', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PEP serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen Lysophosphatidylcholin und genus-level

```{r}
corr_map_genus_LPC <- filter(genus_LI, !is.na(LPC))
 
 corr_spearman_genus_LPC <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_LPC, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$LPC
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$LPC
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$LPC
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_LPC)+1
   
   corr_spearman_genus_LPC[nrow,"FA"] = "LPC"
   
   corr_spearman_genus_LPC[nrow, "Genus"] = i
   
   corr_spearman_genus_LPC[nrow, "p.value"] = p
   
   corr_spearman_genus_LPC[nrow, "rho"] = rho
   
   corr_spearman_genus_LPC[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_LPC[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_LPC[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_LPC[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_LPC$p.adjusted <- p.adjust(corr_spearman_genus_LPC$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_LPC$p.adjusted_PRE <- p.adjust(corr_spearman_genus_LPC$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_LPC$p.adjusted_POST <- p.adjust(corr_spearman_genus_LPC$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_LPC, file = '/Users/student05/Documents/serum lipids/genus/LPC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen Ceramid und genus-level

```{r}
corr_map_genus_CER <- filter(genus_LI, !is.na(CER))
 
 corr_spearman_genus_CER <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_CER, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$CER
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$CER
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$CER
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_CER)+1
   
   corr_spearman_genus_CER[nrow,"FA"] = "CER"
   
   corr_spearman_genus_CER[nrow, "Genus"] = i
   
   corr_spearman_genus_CER[nrow, "p.value"] = p
   
   corr_spearman_genus_CER[nrow, "rho"] = rho
   
   corr_spearman_genus_CER[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_CER[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_CER[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_CER[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_CER$p.adjusted <- p.adjust(corr_spearman_genus_CER$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_CER$p.adjusted_PRE <- p.adjust(corr_spearman_genus_CER$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_CER$p.adjusted_POST <- p.adjust(corr_spearman_genus_CER$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_CER, file = '/Users/student05/Documents/serum lipids/genus/CER.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen Hexosylceramid und genus-level

```{r}
corr_map_genus_HexCer <- filter(genus_LI, !is.na(HexCer))
 
 corr_spearman_genus_HexCer <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_HexCer, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$HexCer
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$HexCer
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$HexCer
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_HexCer)+1
   
   corr_spearman_genus_HexCer[nrow,"FA"] = "HexCer"
   
   corr_spearman_genus_HexCer[nrow, "Genus"] = i
   
   corr_spearman_genus_HexCer[nrow, "p.value"] = p
   
   corr_spearman_genus_HexCer[nrow, "rho"] = rho
   
   corr_spearman_genus_HexCer[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_HexCer[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_HexCer[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_HexCer[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_HexCer$p.adjusted <- p.adjust(corr_spearman_genus_HexCer$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_HexCer$p.adjusted_PRE <- p.adjust(corr_spearman_genus_HexCer$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_HexCer$p.adjusted_POST <- p.adjust(corr_spearman_genus_HexCer$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_HexCer, file = '/Users/student05/Documents/serum lipids/genus/HexCer.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, label= 'Proband',
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Prevotella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
```

Loop und Plots Korrelation zwischen summierten Serumlipiden und genus-level

```{r}
corr_map_genus_Sum <- filter(genus_LI, !is.na(Sum))
 
 corr_spearman_genus_Sum <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_Sum, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$Sum
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$Sum
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$Sum
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_Sum)+1
   
   corr_spearman_genus_Sum[nrow,"FA"] = "Sum"
   
   corr_spearman_genus_Sum[nrow, "Genus"] = i
   
   corr_spearman_genus_Sum[nrow, "p.value"] = p
   
   corr_spearman_genus_Sum[nrow, "rho"] = rho
   
   corr_spearman_genus_Sum[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_Sum[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_Sum[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_Sum[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_Sum$p.adjusted <- p.adjust(corr_spearman_genus_Sum$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_Sum$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Sum$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_Sum$p.adjusted_POST <- p.adjust(corr_spearman_genus_Sum$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_Sum, file = '/Users/student05/Documents/serum lipids/genus/Sum.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 

 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen summierten Membranlipiden und genus-level

```{r}
corr_map_genus_Sum.Membrane <- filter(genus_LI, !is.na(Sum.Membrane))
 
 corr_spearman_genus_Sum.Membrane <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_Sum.Membrane, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$Sum.Membrane
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$Sum.Membrane
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$Sum.Membrane
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_Sum.Membrane)+1
   
   corr_spearman_genus_Sum.Membrane[nrow,"FA"] = "Sum.Membrane"
   
   corr_spearman_genus_Sum.Membrane[nrow, "Genus"] = i
   
   corr_spearman_genus_Sum.Membrane[nrow, "p.value"] = p
   
   corr_spearman_genus_Sum.Membrane[nrow, "rho"] = rho
   
   corr_spearman_genus_Sum.Membrane[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_Sum.Membrane[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_Sum.Membrane[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_Sum.Membrane[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_Sum.Membrane$p.adjusted <- p.adjust(corr_spearman_genus_Sum.Membrane$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_Sum.Membrane$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Sum.Membrane$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_Sum.Membrane$p.adjusted_POST <- p.adjust(corr_spearman_genus_Sum.Membrane$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_Sum.Membrane, file = '/Users/student05/Documents/serum lipids/genus/Sum.Membrane.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 

 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum.Membrane', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Membrane serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen summierten Storagelipiden und genus-level

```{r}
corr_map_genus_Sum.Storage <- filter(genus_LI, !is.na(Sum.Storage))
 
 corr_spearman_genus_Sum.Storage <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_Sum.Storage, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$Sum.Storage
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$Sum.Storage
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$Sum.Storage
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_Sum.Storage)+1
   
   corr_spearman_genus_Sum.Storage[nrow,"FA"] = "Sum.Storage"
   
   corr_spearman_genus_Sum.Storage[nrow, "Genus"] = i
   
   corr_spearman_genus_Sum.Storage[nrow, "p.value"] = p
   
   corr_spearman_genus_Sum.Storage[nrow, "rho"] = rho
   
   corr_spearman_genus_Sum.Storage[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_Sum.Storage[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_Sum.Storage[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_Sum.Storage[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_Sum.Storage$p.adjusted <- p.adjust(corr_spearman_genus_Sum.Storage$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_Sum.Storage$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Sum.Storage$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_Sum.Storage$p.adjusted_POST <- p.adjust(corr_spearman_genus_Sum.Storage$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_Sum.Storage, file = '/Users/student05/Documents/serum lipids/genus/Sum.Storage.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Storage', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Storage serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
```

Loop und Plots Korrelation zwischen summierten Lysolipiden und genus-level

```{r}
corr_map_genus_Sum.Lyso <- filter(genus_LI, !is.na(Sum.Lyso))
 
 corr_spearman_genus_Sum.Lyso <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_Sum.Lyso, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$Sum.Lyso
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$Sum.Lyso
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$Sum.Lyso
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_Sum.Lyso)+1
   
   corr_spearman_genus_Sum.Lyso[nrow,"FA"] = "Sum.Lyso"
   
   corr_spearman_genus_Sum.Lyso[nrow, "Genus"] = i
   
   corr_spearman_genus_Sum.Lyso[nrow, "p.value"] = p
   
   corr_spearman_genus_Sum.Lyso[nrow, "rho"] = rho
   
   corr_spearman_genus_Sum.Lyso[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_Sum.Lyso[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_Sum.Lyso[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_Sum.Lyso[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_Sum.Lyso$p.adjusted <- p.adjust(corr_spearman_genus_Sum.Lyso$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_Sum.Lyso$p.adjusted_PRE <- p.adjust(corr_spearman_genus_Sum.Lyso$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_Sum.Lyso$p.adjusted_POST <- p.adjust(corr_spearman_genus_Sum.Lyso$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_Sum.Lyso, file = '/Users/student05/Documents/serum lipids/genus/Sum.Lyso.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(50, -1.5), xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='Sum.Lyso', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'Sum.Lyso serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen LPC/PE-Verhältnis und genus-level

```{r}
corr_map_genus_LPC.PC <- filter(genus_LI, !is.na(LPC.PC))
 
 corr_spearman_genus_LPC.PC <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_LPC.PC, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$LPC.PC
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$LPC.PC
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$LPC.PC
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_LPC.PC)+1
   
   corr_spearman_genus_LPC.PC[nrow,"FA"] = "LPC.PC"
   
   corr_spearman_genus_LPC.PC[nrow, "Genus"] = i
   
   corr_spearman_genus_LPC.PC[nrow, "p.value"] = p
   
   corr_spearman_genus_LPC.PC[nrow, "rho"] = rho
   
   corr_spearman_genus_LPC.PC[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_LPC.PC[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_LPC.PC[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_LPC.PC[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_LPC.PC$p.adjusted <- p.adjust(corr_spearman_genus_LPC.PC$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_LPC.PC$p.adjusted_PRE <- p.adjust(corr_spearman_genus_LPC.PC$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_LPC.PC$p.adjusted_POST <- p.adjust(corr_spearman_genus_LPC.PC$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_LPC.PC, file = '/Users/student05/Documents/serum lipids/genus/LPC.PC.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 

 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipid ratio', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Bifidobacteriales.f__Bifidobacteriaceae.g__Bifidobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman',cor.coef.coord = c(0.03, -1), xlab= 'LPC/PC serum lipid ratio', ylab = 'log10 (Relative Abundance g__Bifidobacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC.PC serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Porphyromonadaceae.g__Parabacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipid ratio', ylab = 'log10 (Relative Abundance g__Parabacteroides')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='LPC.PC', y='k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'LPC/PC serum lipid ratio', ylab = 'log10 (Relative Abundance g__Lachnospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen CER/SM-Verhältnis und genus-level

```{r}
 corr_map_genus_CER.SM <- filter(genus_LI, !is.na(CER.SM))
 
 corr_spearman_genus_CER.SM <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_CER.SM, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$CER.SM
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$CER.SM
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$CER.SM
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_CER.SM)+1
   
   corr_spearman_genus_CER.SM[nrow,"FA"] = "CER.SM"
   
   corr_spearman_genus_CER.SM[nrow, "Genus"] = i
   
   corr_spearman_genus_CER.SM[nrow, "p.value"] = p
   
   corr_spearman_genus_CER.SM[nrow, "rho"] = rho
   
   corr_spearman_genus_CER.SM[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_CER.SM[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_CER.SM[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_CER.SM[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_CER.SM$p.adjusted <- p.adjust(corr_spearman_genus_CER.SM$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_CER.SM$p.adjusted_PRE <- p.adjust(corr_spearman_genus_CER.SM$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_CER.SM$p.adjusted_POST <- p.adjust(corr_spearman_genus_CER.SM$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_CER.SM, file = '/Users/student05/Documents/serum lipids/genus/CER.SM.txt', sep ="\t", col.names = TRUE,row.names = FALSE)

 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid ratio', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='CER.SM', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Clostridiaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'CER.SM serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 


```

Loop und Plots Korrelation zwischen HexCer/CER-Verhältnis und genus-level

```{r}
corr_map_genus_HexCer.CER <- filter(genus_LI, !is.na(HexCer.CER))
 
 corr_spearman_genus_HexCer.CER <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_HexCer.CER, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$HexCer.CER
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$HexCer.CER
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$HexCer.CER
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_HexCer.CER)+1
   
   corr_spearman_genus_HexCer.CER[nrow,"FA"] = "HexCer.CER"
   
   corr_spearman_genus_HexCer.CER[nrow, "Genus"] = i
   
   corr_spearman_genus_HexCer.CER[nrow, "p.value"] = p
   
   corr_spearman_genus_HexCer.CER[nrow, "rho"] = rho
   
   corr_spearman_genus_HexCer.CER[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_HexCer.CER[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_HexCer.CER[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_HexCer.CER[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_HexCer.CER$p.adjusted <- p.adjust(corr_spearman_genus_HexCer.CER$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_HexCer.CER$p.adjusted_PRE <- p.adjust(corr_spearman_genus_HexCer.CER$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_HexCer.CER$p.adjusted_POST <- p.adjust(corr_spearman_genus_HexCer.CER$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_HexCer.CER, file = '/Users/student05/Documents/serum lipids/genus/HexCer.CER.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 

 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid ratio', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Phascolarctobacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='HexCer.CER', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'HexCer.CER serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

```

Loop und Plots Korrelation zwischen PC/PE-Verhältnis und genus-level

In Arbeit

```{r}
 corr_map_genus_PC.PE <- filter(genus_LI, !is.na(PC.PE))
 
 corr_spearman_genus_PC.PE <- data.frame()
 
 for( i in genus_colnames) {
   
   tmp <- filter(corr_map_genus_PC.PE, !is.na(i))
   
   y = tmp[,i]
   
   x = tmp$PC.PE
   
   tmp_corr_spearman <- cor.test(x, y, method="spearman")
   
   rho = tmp_corr_spearman$estimate
   
   p = tmp_corr_spearman$p.value
   
   z = subset(filter(tmp, Time == "PRE"))[,i]
   
   w = subset(filter(tmp, Time == "PRE"))$PC.PE
   
   tmp_corr_spearman_PRE <- cor.test(z, w, method="spearman")
   
   rho_PRE = tmp_corr_spearman_PRE$estimate
   
   p_PRE = tmp_corr_spearman_PRE$p.value
   
   r = subset(filter(tmp, Time == "POST"))[,i]
   
   s = subset(filter(tmp, Time == "POST"))$PC.PE
   
   tmp_corr_spearman_POST <- cor.test(r, s, method="spearman")
   
   rho_POST = tmp_corr_spearman_POST$estimate
   
   p_POST = tmp_corr_spearman_POST$p.value
   
   nrow = nrow(corr_spearman_genus_PC.PE)+1
   
   corr_spearman_genus_PC.PE[nrow,"FA"] = "PC.PE"
   
   corr_spearman_genus_PC.PE[nrow, "Genus"] = i
   
   corr_spearman_genus_PC.PE[nrow, "p.value"] = p
   
   corr_spearman_genus_PC.PE[nrow, "rho"] = rho
   
   corr_spearman_genus_PC.PE[nrow, "p.value_PRE"] = p_PRE
   
   corr_spearman_genus_PC.PE[nrow, "rho_PRE"] = rho_PRE
   
   corr_spearman_genus_PC.PE[nrow, "p.value_POST"] = p_POST
   
   corr_spearman_genus_PC.PE[nrow, "rho_POST"] = rho_POST
   
 }
 
 corr_spearman_genus_PC.PE$p.adjusted <- p.adjust(corr_spearman_genus_PC.PE$p.value, method = "BH", n = 35)
 
 corr_spearman_genus_PC.PE$p.adjusted_PRE <- p.adjust(corr_spearman_genus_PC.PE$p.value_PRE, method = "BH", n = 35)
 
 corr_spearman_genus_PC.PE$p.adjusted_POST <- p.adjust(corr_spearman_genus_PC.PE$p.value_POST, method = "BH", n = 35)
 
 
 write.table( corr_spearman_genus_PC.PE, file = '/Users/student05/Documents/serum lipids/genus/PC.PE.txt', sep ="\t", col.names = TRUE,row.names = FALSE)
 
 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Oscillospira',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid ratio', ylab = 'log10 (Relative Abundance g__Oscillospira')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Ruminococcaceae.g__Faecalibacterium',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line',conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Faecalibacterium')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text( hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Coprococcus', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Coprococcus')+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Alcaligenaceae.g__Sutterella',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Verrucomicrobia.c__Verrucomicrobiae.o__Verrucomicrobiales.f__Verrucomicrobiaceae.g__Akkermansia',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Sutterella')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dorea')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Veillonellaceae.g__Dialister',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid ratio', ylab = 'log10 (Relative Abundance g__Bacteroides')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(hjust=1))+
   theme(legend.position="none") 
 
 pdf("/Users/student05/Documents/fertige Plots/PC.PE.Proteo.pdf",width=8, height=10)
 ggscatter(phylum_LI, x='PC.PE', y='k__Bacteria.p__Proteobacteria',color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(30, -1.7 ), cor.coef.size = 7,xlab= 'PC/PE Verhältnis', ylab = 'Relatives Vorkommen p__Proteobacteria [%]')+
   facet_grid(.~ Time, scales = "free_x")+
   theme(strip.text.x = element_text(size = 18, colour = "black"))+
   theme(text = element_text(size=18),
         axis.text.x = element_text(hjust=1))+
   scale_y_log10(labels = percent_format())+
   theme(legend.position="none")
 dev.off()
 
 genus_LI$Time <- factor(genus_LI$Time, levels = c("PRE", "POST"))
 
 pdf("/Users/student05/Documents/fertige Plots/PC.PE.bacteroides.pdf",width=8, height=10)
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',size = 2.5,color = 'Time', palette = c('skyblue', 'orchid'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', cor.coef.coord = c(30, -1), cor.coef.size = 7,xlab= 'PC/PE Verhältnis', ylab = 'Relatives Vorkommen g__Bacteroides [%]')+
   facet_grid(.~ Time, scales = "free_x")+
   theme(strip.text.x = element_text(size = 18, colour = "black"))+
   theme(text = element_text(size=18),
         axis.text.x = element_text(hjust=1))+
   scale_y_log10(labels = percent_format())+
   theme(legend.position="none")
 dev.off()
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__.Ruminococcus.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Lachnospiraceae.g__Dorea',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid concentration [nmol/ml]',cor.coef.coord = c(30, -1.1), cor.coef.size = 6, ylab = 'log10 (Relative Abundance g__Bacteroides')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 15, colour = "black"))+
   theme(text = element_text(size=15),
         axis.text.x = element_text(angle=0, hjust=1))+
   theme(legend.position="none") 
 
 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC/PE serum lipid concentration [nmol/ml]',cor.coef.coord = c(30, -1.1), cor.coef.size = 6, ylab = 'log10 (Relative Abundance g__Bacteroides')+
   theme(strip.text.x = element_text(size = 15, colour = "black"))+
   theme(text = element_text(size=15),
         axis.text.x = element_text(angle=0, hjust=1))+
   theme(legend.position="none") 

 ggscatter(genus_LI, x='PC.PE', y='k__Bacteria.p__Firmicutes.c__Erysipelotrichi.o__Erysipelotrichales.f__Erysipelotrichaceae.g__.Eubacterium.',color = 'Time', palette = c('tomato', 'yellowgreen'),  add = 'reg.line', conf.int = TRUE, 
           cor.coef = TRUE, cor.method = 'spearman', xlab= 'PC.PE serum lipid concentration [nmol/ml]', ylab = 'log10 (Relative Abundance g__Dialster')+
   facet_grid(.~ Time,scales = "free_x")+
   theme(strip.text.x = element_text(size = 10, colour = "black"))+
   theme(text = element_text(size=13),
         axis.text.x = element_text(angle=60, hjust=1))+
   theme(legend.position="none") 
```

 
